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REVIEW 3 major objections 5 minor 1 cited by

Citation Sentiment Reflects Multiscale Sociocultural Norms

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Neuroscience citations carry a measurable social fingerprint: collaborators get kinder, outsiders harsher.

desk verdict Plausible large-scale citation-sentiment study; missing classifier validation keeps the cultural claims provisional. read the letter →

arxiv 2411.09675 v2 pith:MYLYE32P submitted 2024-11-14 cs.SI

Xiaohuan Xia , Mathieu Ouellet , Shubhankar P. Patankar , Diana I. Tamir , Dani S. Bassett This is my paper · ORCID
classification cs.SI
keywords citationsentimentanalysislargelanguagemodelssocioculturalnormscollaborationnetworksh-indexindividualismpowerdistance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Scientists rarely cite neutrally, and this paper argues that the warmth or sharpness of a citation is socially structured rather than random. Analyzing hundreds of thousands of citation sentences, the authors show that researchers cite their collaborators more favorably and less critically than they cite strangers, and that higher-status scholars are especially critical of lower-status outsiders. The same pattern reappears at larger scales: wet-lab and review-heavy subfields are gentler, while countries high in individualism and low in power distance produce more critical citations. The paper therefore tries to establish that citation sentiment is a multiscale record of ingroup/outgroup bias, status hierarchy, and cultural norms in science, not just a byproduct of the cited content.

What carries the argument

The central machinery is a sentiment-labeled citation network: each citation edge between two last authors is tagged favorable, neutral, or critical by a large language model fine-tuned on a small human-annotated set, with negative sentiment taking precedence when a paper cites another multiple times. To separate content effects from social effects, the analysis uses a null model that predicts expected sentiment from title similarity, citation frequency, and article type, so all reported results are residual deviations from chance. This machinery lets the authors compare sentiment across collaboration distances, h-index differences, disciplines scored by benchwork and synthesis, countries scored by individualism and power-distance, and author gender.

What would settle it

Take a stratified random sample of roughly one thousand citation sentences from the same corpus, have trained annotators label them as favorable, neutral, or critical, and compare with the model's labels. If agreement is poor or if the reported correlations with collaboration distance, h-index, discipline, and country disappear when using the human labels, the central claim fails.

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Extended reading notes

Core claim

The paper's central discovery is that the sentiment expressed in a citation—favorable, neutral, or critical—varies systematically with the social and cultural positions of the citing and cited authors. On a large sample of neuroscience papers, the authors find that collaboration distance predicts sentiment: collaborators receive more favorable and less critical citations, and pairs who will begin collaborating in the future show elevated critical sentiment just beforehand. Outside collaborations, the greater the citer's h-index relative to the citee's, the more critical the citation. At the discipline level, criticality decreases with benchwork (wet-lab) practice and with the proportion of review articles, and at the country level, critical sentiment rises with individualism and falls with power-distance. The authors conclude that citation sentiment tracks sociocultural norms across scales, with hierarchy and group membership playing a focal role.

Load-bearing premise

The load-bearing premise is that the large language model's favorable/neutral/critical labels are accurate and consistent across disciplines, countries, genders, and time periods; the paper does not report held-out accuracy or inter-annotator agreement for the fine-tuned classifier.

Editorial extensions

If this is right

  • Citation analyses that count only presence or absence will systematically miss the social signal; sentiment-aware counts should differ by collaboration and status.
  • Critical citations are concentrated among high-h-index authors citing lower-h-index non-collaborators, so scientific hierarchy shapes which work receives public challenge.
  • Disciplinary culture appears to modulate criticality: wet-lab disciplines and review-heavy fields cite more gently, suggesting that synthesis practices may cool polarization.
  • Country-level differences in individualism and power-distance predict citation tone, so cross-national bibliometric comparisons should adjust for cultural norms.
  • Men in this dataset cite with more sentiment overall while women show a stronger favoritism bias toward collaborators, indicating that gender shapes the emotional register of citations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If critical citations tend to precede first collaborations, as the temporal pattern suggests, citation sentiment could be mined as a forward-looking signal for team formation; the authors flag this possibility but do not test it prospectively.
  • Because the dataset is confined to neuroscience, the cleanest testable extension is to repeat the pipeline in older, more crystallized disciplines, where the multiscale effects should be attenuated if the mechanism is sociocultural maturity.
  • The same sentiment-labeled network could be turned into a bias-audit instrument: journals, funders, or institutions could monitor whether critical or favorable tone is systematically skewed by gender, nationality, or status, a practical use the paper does not develop.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript assembles a corpus of 627,108 citation sentences from neuroscience articles in the PubMed Central Open Access subset, labels each citation as favorable, neutral, or critical using a fine-tuned GPT-3.5-Turbo model, aggregates labels by citer-citee pair, and compares observed sentiment proportions against a null model that conditions on title similarity, citation frequency, and article type. It reports that researchers cite collaborators more favorably and less critically than non-collaborators, that high h-index authors critically cite lower h-index non-collaborators, that wetlab and high-synthesis disciplines are less critical, that countries high in individualism and low in power distance produce more critical citations, and that men use more sentiment overall while women show a stronger collaborator bias. The paper interprets these patterns as evidence that citation sentiment tracks sociocultural norms across individual, disciplinary, and national scales.

Significance. If the sentiment labels are valid, this is a substantial large-scale descriptive contribution to the science of science. The strengths are the size of the corpus, the explicit construction of a null model that removes some baseline effects of content similarity, citation frequency, and article type, and the clear separation of collaborator versus non-collaborator contrasts across scales. The paper also makes falsifiable predictions, such as the drop in critical sentiment after collaboration begins, that could be tested in other fields. However, the central claim rests entirely on the accuracy and measurement invariance of an LLM-based sentiment classifier whose validation is not reported. Because the aggregate effects are small relative to the base rates, differential measurement error across disciplines, countries, or author groups could produce or distort the main results. The manuscript would be acceptable only after the classifier validation and confound controls are added and shown to preserve the reported patterns.

major comments (3)
  1. [V.B (Measuring Sentiment)] The paper reports no validation of the sentiment classifier. It states only that fine-tuning on 300 manually annotated sentences produced "an improvement of 28% over the baseline (see Supplement)", but it does not define the baseline, report held-out accuracy, per-class precision/recall, inter-annotator agreement, or a confusion matrix. Since only 7.94% of the aggregated citations are critical, even small differential error in detecting critical language—for example, across national academic writing styles or disciplinary registers—can move the slopes in Figures 4 and 6 as much as the reported effects. The supplement is referenced repeatedly but is not part of the submitted text, so these metrics cannot be checked. Please add stratified held-out evaluation by discipline, country, gender, and collaborator status, and show how the main results change under alternative labeling thresholds and aggregation rules.
  2. [V.E (Departmental and Cultural Measures) with II.C-D] The null model conditions only on title-similarity bins, citation frequency, and article type. It does not control for discipline, country, journal, h-index composition, or author seniority. The disciplinary and country analyses use aggregate means with n=27 and n=23, respectively; if high-individualism countries are overrepresented in drylab disciplines, in high-h-index author pools, or in particular journals, the country correlations in Figure 6 could reflect compositional confounding rather than a cultural effect. The manuscript should report the composition of the country and discipline aggregates and add control analyses using fixed effects, matched samples, or residualization.
  3. [II.C-D and Figures 4-8] Most significance tests are reported as one-sided F-tests, and many related correlations are tested across favorable/critical and collaborator/non-collaborator splits without any multiple-comparison correction. This inflates the number of nominally significant results. Please report two-sided p-values or explicit permutation tests, pre-specify directional hypotheses where possible, and apply a multiple-comparison adjustment or clearly justify why it is unnecessary. Several slopes in Figures 4D-F also have wide confidence intervals, so the strength of the claims should be calibrated to the precision actually achieved.
minor comments (5)
  1. [III.B (Discussion)] "Citation sentiment tracts disciplinary differences" should be "tracks disciplinary differences".
  2. [II.E and Figure 8] The text and figure use "woman" where "women" is intended; please correct the grammar.
  3. [V.B] The phrase "improvement of 28% over the baseline" needs a precise definition of the baseline model, the evaluation split, and the metric being improved; the current sentence is not checkable.
  4. [Figure 8] The units and definitions of the quantities in panel A are unclear; please specify whether they are percentage differences from the null model and what the error bars represent.
  5. [General] No data or code availability statement is provided. Given the reliance on proprietary LLM APIs, the authors should at minimum release the prompts, the 300 annotated sentences, and the analysis code to allow independent replication.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical correlations are derived from measured sentiment labels and a null model whose controls (title similarity, citation frequency, article type) do not include the sociocultural predictors.

full rationale

The paper's central claims are empirical correlations between LLM-derived citation sentiment and collaboration, h-index, discipline, and country variables, benchmarked against a null model. The null model controls for title similarity, citation frequency, and article type (Methods, 'Measuring Sentiment'), none of which are the target sociocultural predictors, so the reported sentiment residuals are not equal by construction to the independent variables. The sentiment labels come from GPT-3.5-Turbo fine-tuned on 300 manually annotated citations; although the absence of reported held-out accuracy and per-stratum differential-error checks is a genuine validity risk, this is a measurement-bias concern, not a circular reduction. The paper also contains self-citations to Bassett and coauthors, but they are ordinary literature citations; no load-bearing uniqueness theorem or ansatz is imported from those works, and no fitted parameter is renamed as a prediction. I therefore find no step that reduces to the paper's own inputs.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The analysis assumes that GPT-3.5's sentiment labels are valid, that coauthorship and h-index proxy social relationships and status, that Hofstede country scores apply to individual citing authors, and that LLM-based benchwork scores measure a discipline's level of explanation. No new entities are introduced.

free parameters (3)
  • Null model bin probabilities = MLE estimates for 54 bins (title similarity x citation frequency x article type)
    These probabilities define expected sentiment ratios; all reported normalized sentiments are deviations from them. If the bins are misspecified, every result changes.
  • Fine-tuning label thresholds = -0.4 and +0.4 on mean annotator scores
    Hand-chosen thresholds convert average annotator sentiment into negative/neutral/positive labels for the 300 fine-tuning examples.
  • Sentiment aggregation precedence = negative > positive > neutral
    A hand-chosen rule that overrides all positive/neutral citations for a citer-citee pair if any negative citation exists; affects the distribution of aggregated sentiment.
assumptions (5)
  • domain assumption GPT-3.5-Turbo sentiment labels correspond to true citation sentiment
    The entire measurement depends on LLM labels. The paper reports only a 28% improvement after fine-tuning on 300 examples, with no held-out accuracy or inter-annotator agreement.
  • domain assumption Coauthorship is a valid proxy for collaboration
    Collaboration network and distance are built from coauthorship edges; the paper acknowledges that not all collaborations yield coauthorship (Discussion III A).
  • domain assumption h-index is a valid proxy for dominance/status
    h-index is used to classify 'higher' and 'lower' status scholars; no seniority measure is available (Discussion III A).
  • domain assumption Hofstede power-distance and individualism scores apply to last authors by country
    Country-level regressions assign Hofstede scores to the country of the citing last author; this is an ecological assumption and the scores are dated.
  • domain assumption Benchwork score from LLM classification measures level of explanation
    Benchwork score is the fraction of 100 sampled papers per department classified as wet-lab by an un-finetuned GPT-3.5 model; no validation is reported.

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Cite this review

Pith. "Pith review of Citation Sentiment Reflects Multiscale Sociocultural Norms." pith.science (2026). https://pith.science/paper/MYLYE32P

@misc{pith2026241109675,
  author       = {Pith},
  title        = {Pith review of: Citation Sentiment Reflects Multiscale Sociocultural Norms},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MYLYE32P}},
  note         = {Machine review of arXiv:2411.09675}
}
read the original abstract

Modern science is formally structured around scholarly publication, where scientific knowledge is canonized through citation. Precisely how citations are given and accrued can provide information about the value of discovery, the history of scientific ideas, the structure of fields, and the space or scope of inquiry. Yet parsing this information has been challenging because citations are not simply present or absent; rather, they differ in purpose, function, and sentiment. In this paper, we investigate how critical and favorable sentiments are distributed across citations, and demonstrate that citation sentiment tracks sociocultural norms across scales of collaboration, discipline, and country. At the smallest scale of individuals, we find that researchers cite scholars they have collaborated with more favorably (and less critically) than scholars they have not collaborated with. Outside collaborative relationships, higher h-index scholars cite lower h-index scholars more critically. At the mesoscale of disciplines, we find that wetlab disciplines tend to be less critical than drylab disciplines, and disciplines that engage in more synthesis through publishing more review articles tend to be less critical. At the largest scale of countries, we find that greater individualism (and lesser acceptance of the unequal distribution of power) is associated with more critical sentiment. Collectively, our results demonstrate how sociocultural factors can explain variations in sentiment in scientific communication. As such, our study contributes to a broader understanding of how human factors influence the practice of science, and underscores the importance of considering the larger sociocultural contexts in which science progresses.

Figures

Figures reproduced from arXiv: 2411.09675 by the authors.

Figure 1
Figure 1. Sentiment analysis in citation and author networks. A two-layer network comprising a network of cita￾tions and a network of collaborators. In the citation network, nodes represent scientific articles, and edges represent cita￾tions. Sentiment analysis categorizes each edge as favorable (blue), neutral (grey), or critical (red). In the collaboration network, nodes represent scholars, and scholars who co-author articl… view at source ↗
Figure 2
Figure 2. Citation sentiment depends on distance and years since (or to) first collaboration. (A) Citation sen￾timent relative to the null model as a function of the collab￾oration distance between the last authors of the citing and cited works. Shaded regions denote one standard deviation from the mean at a given collaboration distance. (B) Cita￾tion sentiment as a function of the number of years since the first collaboratio… view at source ↗
Figure 4
Figure 4. Critical citation sentiment varies across disciplines. (A) Mean critical sentiment in a discipline toward collaborators versus non-collaborators. (B) Mean critical sentiment as a function of the benchwork score of the citing paper’s discipline. We define the benchwork score as the estimated proportion of wet lab papers (as opposed to dry lab papers) in a discipline. Orange corresponds to the sentiment toward non-col… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Favorable citation sentiment varies across disciplines. (A) Mean favorable sentiment in a discipline toward collaborators versus non-collaborators. (B) Mean favorable sentiment as a function of the benchwork score of the citing paper’s discipline. We define benchwork s…
Figure 6
Figure 6. Figure 6: Critical sentiment varies across countries. (A) Mean critical sentiment across countries toward collaborators versus non-collaborators. (B) Mean critical sentiment for countries as a function of the degree to which people accept the unequal distribution of power, measu…
Figure 7
Figure 7. Figure 7: Favorable sentiment varies across countries. (A) Mean favorable sentiment across countries toward collaborators versus non-collaborators. (B) Mean favorable sentiment for countries as a function of the degree to which people accept the unequal distribution of power, me…
Figure 8
Figure 8. Figure 8: Citation sentiment varies by gender. (A) Mean and bias in citation sentiment separated by sentiment type and author gender. Asterisks indicate p-values in two-sided Welch’s t-tests; a single asterisk indicates p < 0.05; two asterisks indicate p < 0.01; three asterisks …

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Causal Language in Observational Studies: Sociocultural Backgrounds and Team Composition

    physics.soc-ph 2025-02 conditional novelty 6.0 of 10

    Causal language in observational-study abstracts is more common among less experienced authors, smaller teams, male last authors, and authors from high-uncertainty-avoidance countries.

Reference graph

Works this paper leans on

164 extracted references · 75 canonical work pages · cited by 1 Pith paper

  1. [1]

    Datson and P

    L. Datson and P. L. Galison, Objectivity (Princeton Uni- versity Press, 2010)

    2010

  2. [2]

    Reiss and J

    J. Reiss and J. Sprenger, Scientific Objectivity, in The Stanford Encyclopedia of Philosophy , edited by E. N. Zalta (Metaphysics Research Lab, Stanford University,

  3. [3]

    Daston, Objectivity and the escape from perspective, Social Studies of Science 22, 597 (1992)

    L. Daston, Objectivity and the escape from perspective, Social Studies of Science 22, 597 (1992)

    1992

  4. [4]

    Freese and D

    J. Freese and D. Peterson, The emergence of statistical objectivity: Changing ideas of epistemic vice and virtue in science, Sociological Theory 36, 289 (2018)

    2018

  5. [5]

    Douglas, Facts, values, and objectivity, The SAGE Handbook of the Philosophy of Social Sciences , 513 (2011)

    H. Douglas, Facts, values, and objectivity, The SAGE Handbook of the Philosophy of Social Sciences , 513 (2011)

    2011

  6. [6]

    H. E. Longino, Science as Social Knowledge: Values and Objectivity in Scientific Inquiry (Princeton University Press, 1990) p. 76

    1990

  7. [7]

    S. G. Harding, Objectivity and Diversity: Another Logic of Scientific Research (University of Chicago Press, 2015)

    2015

  8. [8]

    I. K. Sheppard, Subjectivities of the scientific endeavor: Noting the illusion of objectivity in the history of science and medicine, Medical history and medical philosophy 1 (2022)

    2022

Show all 164 references
  1. [9]

    Harding, Rethinking standpoint epistemology: What is ”strong objectivity”?, The Centennial Review 36, 437 (1992)

    S. Harding, Rethinking standpoint epistemology: What is ”strong objectivity”?, The Centennial Review 36, 437 (1992)

    1992

  2. [10]

    D. B. Resnik and K. C. Elliott, The ethical challenges of socially responsible science, Account Res. 23, 31 (2016)

    2016

  3. [11]

    Harding, After mr

    S. Harding, After mr. nowhere: What kind of proper self for a scientist?, Feminist Philosophy Quarterly 1, 2 (2015)

    2015

  4. [12]

    C. S. Crandall and M. Schaller, Scientists and sci- ence: How individual goals shape collective norms, in The Psychological Foundations of Culture , edited by M. Schaller and C. S. Crandall (Imprint Psychology Press, 2003)

    2003

  5. [13]

    Daston, Cloud physiognomy, Representations 135, 45 (2016)

    L. Daston, Cloud physiognomy, Representations 135, 45 (2016)

    2016

  6. [14]

    Gazni, C

    A. Gazni, C. R. Sugimoto, and F. Didegah, Mapping world scientific collaboration: Authors, institutions, and countries, J. Am. Soc. Inf. Sci. 63, 323 (2012)

    2012

  7. [15]

    T. K. Gilbert and A. Loveridge, Subjectifying objectiv- ity: Delineating tastes in theoretical quantum gravity research, Social Studies of Science 51, 73 (2021)

    2021

  8. [16]

    Larivi` ere, E

    V. Larivi` ere, E. Archambault, and Y. Gingras, Long- term variations in the aging of scientific literature: From exponential growth to steady-state science (1900–2004), J. Am. Soc. Inf. Sci. 59, 288 (2008)

    2008

  9. [17]

    Larivi` ere, Y

    V. Larivi` ere, Y. Gingras, and E. Archambault, The de- cline in the concentration of citations, 1900–2007, J. Am. Soc. Inf. Sci. 60, 858 (2009)

    2009

  10. [18]

    J. M. Budd, Citations and knowledge claims: sociology of knowledge as a case in point, Journal of Information Science 25, 265 (1999)

    1999

  11. [19]

    Bruggeman, V

    J. Bruggeman, V. A. Traag, and J. Uitermark, Detect- ing communities through network data, American Soci- ological Review 77, 1050 (2012)

    2012

  12. [20]

    Mitra, M

    S. Mitra, M. Palmer, and V. Vuong, Development and interdisciplinarity: A citation analysis, World Develop- ment 135, 105076 (2020)

    2020

  13. [21]

    K. S. Ray, P. Zurn, J. D. Dworkin, D. S. Bassett, and D. B. Resnik, Citation bias, diversity, and ethics, Ac- count Res 31, 158 (2024)

    2024

  14. [22]

    A. G. Greenwald and E. S. Schuh, An ethnic bias in sci- entific citations, European Journal of Social Psychology 24, 623 (1994)

    1994

  15. [23]

    E. Y. Li, C. H. Liao, and H. R. Yen, Co-authorship net- works and research impact: A social capital perspective, Research Policy 42, 1515 (2013)

    2013

  16. [24]

    A. C. Morgan, D. J. Economou, S. F. Way, and A. Clauset, Prestige drives epistemic inequality in the diffusion of scientific ideas, EPJ Data Sci 7 (2018)

    2018

  17. [25]

    Whissell, Linguistic complexity of abstracts and titles in highly cited journals, Perceptual and Motor Skills88, 76 (1999)

    C. Whissell, Linguistic complexity of abstracts and titles in highly cited journals, Perceptual and Motor Skills88, 76 (1999)

    1999

  18. [26]

    Espinosa-Rada, E

    A. Espinosa-Rada, E. Bellotti, M. G. Everett, and C. Stadtfeld, Co-evolution of a socio-cognitive scientific network: A case study of citation dynamics among as- tronomers, Social Networks 78, 92 (2024)

    2024

  19. [27]

    E. G. Teich, J. Z. Kim, C. W. Lynn, S. C. Simon, A. A. Klishin, K. P. Szymula, P. Srivastava, L. C. Bassett, P. Zurn, J. D. Dworkin, and D. S. Bassett, Citation in- equity and gendered citation practices in contemporary physics, Nature Physics 18, 1161 (2022)

    2022

  20. [28]

    Hicks and J

    D. Hicks and J. Potter, Sociology of scientific knowledge: A reflexive citation analysis or science disciplines and disciplining science, Social Studies of Science 21, 459 (1991)

    1991

  21. [29]

    Restivo and J

    S. Restivo and J. Loughlin, Critical sociology of science and scientific validity, Knowledge 8, 486 (1987). 15

    1987

  22. [30]

    Giuffrida, G

    C. Giuffrida, G. Abramo, and C. A. D’Angelo, Are all citations worth the same? Valuing citations by the value of the citing items, Journal of Informetrics 13, 500 (2019)

    2019

  23. [31]

    W. R. Shadish, D. Tolliver, M. Gray, and S. K. S. Gupta, Author judgements about works they cite: Three stud- ies from psychology journals, Social Studies of Science 25, 477 (1995)

    1995

  24. [32]

    M. A. Safer and R. Tang, The psychology of referencing in psychology journal articles, Perspectives on Psycho- logical Science 4, 51 (2009)

    2009

  25. [33]

    Roth and M

    W.-M. Roth and M. Cole, The referencing practices of Mind, Culture, and Activity : On citing (sighting?) and being cited (sighted?), Mind, Culture, and Activity 17, 93 (2010)

    2010

  26. [34]

    M. G. Erikson and P. Erlandson, A taxonomy of motives to cite, Social Studies of Science 44, 625 (2014)

    2014

  27. [35]

    Mar ´ ı-S´ aez and G

    V.-M. Mar ´ ı-S´ aez and G. Ceballos-Castro, Opening the black box of citations: A qualitative analysis on the basis of the taxonomy of Erikson and Erlandson, Com- munication Theory 29, 463 (2019)

    2019

  28. [36]

    Small, On the shoulders of robert merton: Towards a normative theory of citation, Scientometrics 60, 71 (2004)

    H. Small, On the shoulders of robert merton: Towards a normative theory of citation, Scientometrics 60, 71 (2004)

    2004

  29. [37]

    Yousif, Z

    A. Yousif, Z. Niu, J. K. Tarus, and A. Ahmad, A survey on sentiment analysis of scientific citations, Artificial Intelligence Review 52, 1805 (2019)

    2019

  30. [38]

    J. M. Nicholson, M. Mordaunt, P. Lopez, A. Uppala, D. Rosati, N. P. Rodrigues, P. Grabitz, and S. C. Rife, Scite: A smart citation index that displays the context of citations and classifies their intent using deep learn- ing, Quantitative Science Studies 2, 882 (2021)

    2021

  31. [39]

    K. Ravi, S. Setlur, V. Ravi, and V. Govindaraju, Article citation sentiment analysis using deep learning, in 2018 IEEE 17th International Conference on Cognitive In- formatics & Cognitive Computing (ICCI* CC) (IEEE,

    2018

  32. [40]

    Hariri, Sentiment analysis of citations in scientific articles using chatgpt: Identifying potential biases and conflicts of interest, arXiv preprint arXiv:2404.01800 (2024)

    W. Hariri, Sentiment analysis of citations in scientific articles using chatgpt: Identifying potential biases and conflicts of interest, arXiv preprint arXiv:2404.01800 (2024)

    2024

  33. [41]

    R. M. E. Sabbatini and S. H. Cardoso, Interdisciplinar- ity in the neurosciences, Interdisciplinary Science Re- views 27, 303 (2002)

    2002

  34. [42]

    Bilyk and A

    V. Bilyk and A. Dimitrova, Neurobiology as an interdis- ciplinary science in the system of natural sciences, Broad Research in Artificial Intelligence and Neuroscience 13, 1 (2022)

    2022

  35. [43]

    M. E. Newman, Who is the best connected scientist? a study of scientific coauthorship networks, Phys. Rev. E 64 (2001)

    2001

  36. [44]

    Huber, Disciplinary cultures and social reproduction, European Journal of Education 25, 241 (1990)

    L. Huber, Disciplinary cultures and social reproduction, European Journal of Education 25, 241 (1990)

    1990

  37. [45]

    Becher, Towards a definition of disciplinary cultures, Studies in Higher Education 6, 109 (1981)

    T. Becher, Towards a definition of disciplinary cultures, Studies in Higher Education 6, 109 (1981)

    1981

  38. [46]

    V¨ alimaa, Culture and identity in higher education research, Higher Education 36, 119 (1998)

    J. V¨ alimaa, Culture and identity in higher education research, Higher Education 36, 119 (1998)

    1998

  39. [47]

    Stichweh, The sociology of scientific disciplines: On the genesis and stability of the disciplinary structure of modern science, Science in Context 5, 3 (1992)

    R. Stichweh, The sociology of scientific disciplines: On the genesis and stability of the disciplinary structure of modern science, Science in Context 5, 3 (1992)

    1992

  40. [48]

    Pinch, The culture of scientists and disciplinary rhetoric, European Journal of Education 25, 295 (1990)

    T. Pinch, The culture of scientists and disciplinary rhetoric, European Journal of Education 25, 295 (1990)

    1990

  41. [49]

    Hagge, Disciplinary style manuals as reliable guides to scientific discourse norms, Technical Communication 44, 129 (1997)

    J. Hagge, Disciplinary style manuals as reliable guides to scientific discourse norms, Technical Communication 44, 129 (1997)

    1997

  42. [50]

    Oppenheim and H

    P. Oppenheim and H. Putnam, Unity of science as a working hypothesis, (1958)

    1958

  43. [51]

    Potochnik and B

    A. Potochnik and B. McGill, The limitations of hi- erarchical organization, Philosophy of Science 79, 120 (2012)

    2012

  44. [52]

    Sober, The multiple realizability argument against reductionism, Philosophy of Science , 542 (1999)

    E. Sober, The multiple realizability argument against reductionism, Philosophy of Science , 542 (1999)

    1999

  45. [53]

    L. N. Ross and D. S. Bassett, Causation in neuroscience: keeping mechanism meaningful, Nat Rev Neurosci 25, 81 (2024)

    2024

  46. [54]

    Craver, J

    C. Craver, J. Tabery, and P. Illari, Mechanisms in Sci- ence, in The Stanford Encyclopedia of Philosophy, edited by E. N. Zalta and U. Nodelman (Metaphysics Research Lab, Stanford University, 2024) Fall 2024 ed

    2024

  47. [55]

    van Riel and R

    R. van Riel and R. Van Gulick, Scientific Reduction, in The Stanford Encyclopedia of Philosophy , edited by E. N. Zalta and U. Nodelman (Metaphysics Research Lab, Stanford University, 2024) Spring 2024 ed

    2024

  48. [56]

    R. K. Garrett, S. D. Gvirsman, B. Johnson, Y. Tsfati, R. Neo, and A. Dal, Implications of pro- and counter- attitudinal information exposure for affective polariza- tion, Human Communication Research 40, 309 (2014)

    2014

  49. [57]

    J. N. Price, Vincent Cappella and L. Nir, Does disagree- ment contribute to more deliberative opinion?, Political Communication 19, 95 (2002)

    2002

  50. [58]

    Porter and K

    T. Porter and K. Schumann, Intellectual humility and openness to the opposing view, Self and Identity17, 139 (2017)

    2017

  51. [59]

    C. R. Sunstein, Republic.com 2.0 (Princeton University Press, 2007)

    2007

  52. [60]

    Wojcieszak, S

    M. Wojcieszak, S. Winter, and X. Yu, Social norms and selectivity: Effects of norms of open-mindedness on con- tent selection and affective polarization., Mass Commu- nication and Society 23, 455 (2020)

    2020

  53. [61]

    Baehr, The structure of open-mindedness, Canadian Journal of Philosophy 41, 191 (2011)

    J. Baehr, The structure of open-mindedness, Canadian Journal of Philosophy 41, 191 (2011)

    2011

  54. [62]

    A. P. Christensen, K. N. Cotter, and P. J. Silvia, Re- opening openness to experience: A network analysis of four openness to experience inventories, Journal of per- sonality assessment 101, 574 (2019)

    2019

  55. [63]

    Dieffenbach and M

    M. Dieffenbach and M. D. Lieberman, Belief bubbles: How latitudes of acceptance shape social decisions, psyarxiv (2023)

    2023

  56. [64]

    Leslie, A

    S.-J. Leslie, A. Cimpian, M. Meyer, and E. Freeland, Expectations of brilliance underlie gender distributions across academic disciplines, Science 347, 262 (2015)

    2015

  57. [65]

    Muradoglu, Z

    M. Muradoglu, Z. Horne, M. D. Hammond, S.-J. Leslie, and A. Cimpian, Women—particularly underrep- resented minority women—and early-career academics feel like impostors in fields that value brilliance, Journal of Educational Psychology 114, 1086 (2022)

    2022

  58. [66]

    Hofstede, Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations (Sage publications, 2001)

    G. Hofstede, Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations (Sage publications, 2001)

    2001

  59. [67]

    Siepmann, Academic writing and culture: An overview of differences between english, french and ger- man, Meta 51, 131 (2006)

    D. Siepmann, Academic writing and culture: An overview of differences between english, french and ger- man, Meta 51, 131 (2006)

    2006

  60. [68]

    Kubota, Cross-cultural perspectives on writing: Con- trastive rhetoric, in Sociolinguistics and Language Edu- cation, edited by N

    R. Kubota, Cross-cultural perspectives on writing: Con- trastive rhetoric, in Sociolinguistics and Language Edu- cation, edited by N. H. Hornberger and S. L. McKay (Bristol, Blue Ridge Summit: Multilingual Matters,

  61. [69]

    Atkinson, Intercultural rhetoric and intercultural communication, in The Routledge Handbook of Language and Intercultural Communication, edited by J

    D. Atkinson, Intercultural rhetoric and intercultural communication, in The Routledge Handbook of Language and Intercultural Communication, edited by J. Jackson (Routledge, 2020)

    2020

  62. [70]

    D. Atkinson, Contrasting rhetorics/contrasting cul- tures: Why contrastive rhetoric needs a better concep- tualization of culture, Journal of English for Academic Purposes 3, 277 (2004)

    2004

  63. [71]

    Atkinson, Writing and culture in the post-process era, Journal of Second Language Writing 12, 49 (2003)

    D. Atkinson, Writing and culture in the post-process era, Journal of Second Language Writing 12, 49 (2003)

    2003

  64. [72]

    Ramanathan and D

    V. Ramanathan and D. Atkinson, Individualism, aca- demic writing, and ESL writers, Journal of Second Lan- guage Writing 8, 45 (1999)

    1999

  65. [73]

    S. C. Guntuku, T. Talhelm, G. Sherman, A. Fan, S. Giorgi, L. Wei, and L. H. Ungar, Historical patterns of rice farming explain modern-day language use in china and japan more than modernization and urbanization, arXiv preprint arXiv:2308.15352 (2023)

    2023 arXiv

  66. [74]

    Hu and G

    G. Hu and G. Wang, Disciplinary and ethnolinguistic influences on citation in research articles, Journal of En- glish for Academic Purposes 14, 14 (2014)

    2014

  67. [75]

    Fløttum, T

    K. Fløttum, T. Dahl, and T. Kinn, Academic Voices: Across languages and disciplines (John Benjamins, 2006)

    2006

  68. [76]

    Fløttum, Variation of stance and voice across cul- tures, in Stance and Voice in Written Academic Genres, edited by K

    K. Fløttum, Variation of stance and voice across cul- tures, in Stance and Voice in Written Academic Genres, edited by K. Hyland and C. Guinda (Palgrave Macmil- lan, 2012)

    2012

  69. [77]

    Taylor and T

    G. Taylor and T. Chen, Linguistic, cultural and sub- cultural issues in contrastive discourse analysis: An- glo–American and Chinese scientific texts, Applied Lin- guistics 12, 319 (1991)

    1991

  70. [78]

    the results might not fully represent

    S. A. Sun and F. K. Jiang, “the results might not fully represent. . . ”: Negation in the limitations sections of doctoral theses by chinese and american students, Text & Talk (2024)

    2024

  71. [79]

    Hofstede, Geert Hofstede— Hofstede dimension data matrix (2015)

    G. Hofstede, Geert Hofstede— Hofstede dimension data matrix (2015)

    2015

  72. [80]

    S. M. Deats and L. T. Lenker, Gender and academe: Feminist pedagogy and politics (Rowman & Littlefield, 1994)

    1994

  73. [81]

    Ghafoori and R

    N. Ghafoori and R. Oghbatalab, A comparative study of metadiscourse in academic writing: Male vs. female au- thors of research articles in applied linguistics, Journal of Applied Linguistics 5, 87 (2012)

    2012

  74. [82]

    Robson, B

    J. Robson, B. Francis, and B. Read, Writes of passage: Stylistic features of male and female undergraduate his- tory essays, Journal of Further and Higher Education 26, 351 (2002)

    2002

  75. [83]

    Sala and L

    M. Sala and L. D’Angelo, Gender- and authority-related writing performance in academic research articles, Tex- tus 22, 59 (2009)

    2009

  76. [84]

    gendered

    S. Earl-Novell, “gendered” styles of writing and the “in- equality in assessment” hypothesis: an explanation for gender differentiation in first class academic achieve- ment at university, International Journal of Sociology and Social Policy 21, 160 (2001)

    2001

  77. [85]

    Larivi` ere, C

    V. Larivi` ere, C. Ni, Y. Gingras, B. Cronin, and C. R. Sugimoto, Bibliometrics: Global gender disparities in science, Nature 504, 211 (2013)

    2013

  78. [86]

    Chatterjee and R

    P. Chatterjee and R. M. Werner, Gender disparity in citations in high-impact journal articles, JAMA Netw Open 4, e2114509 (2021)

    2021

  79. [87]

    J. D. Dworkin, K. A. Linn, E. G. Teich, P. Zurn, R. T. Shinohara, and D. S. Bassett, The extent and drivers of gender imbalance in neuroscience reference lists, Nat Neurosci 23, 918 (2020)

    2020

  80. [89]

    Caplar, S

    N. Caplar, S. Tacchella, and S. Birrer, Quantitative eval- uation of gender bias in astronomical publications from citation counts, Nature Astronomy 1 (2017)

    2017

  81. [90]

    K. H. Kang, National Science Foundation, Survey of Earned Doctorates (2023)

    2023

  82. [91]

    Cheryan, S

    S. Cheryan, S. A. Ziegler, A. K. Montoya, and L. Jiang, Why are some STEM fields more gender balanced than others?, Psychol Bull 143, 1 (2017)

    2017

  83. [92]

    Bazerman, Scientific writing as a social act: A review of the literature of the sociology of science, in New Es- says in Technical and Scientific Communication , edited by P

    C. Bazerman, Scientific writing as a social act: A review of the literature of the sociology of science, in New Es- says in Technical and Scientific Communication , edited by P. Anderson, J. Brockman, and C. Miller (Routledge, 1983)

    1983

  84. [93]

    D. L. Medin and M. Bang, The cultural side of science communication, Proceedings of the National Academy of Sciences 111, 13621 (2014)

    2014

  85. [94]

    Goldbort, Scientific english, in Writing for Science (Yale University Press, 2006)

    R. Goldbort, Scientific english, in Writing for Science (Yale University Press, 2006)

    2006

  86. [95]

    Thompson and Y

    G. Thompson and Y. Ye, Evaluation in the reporting verbs used in academic papers, Applied Linguistics 12, 305 (1991)

    1991

  87. [96]

    Y. Ding, G. Zhang, T. Chambers, M. Song, X. Wang, and C. Zhai, Content-based citation analysis: The next generation of citation analysis, Journal of the associ- ation for information science and technology 65, 1820 (2014)

    2014

  88. [97]

    Teufel, A

    S. Teufel, A. Siddharthan, and D. Tidhar, Automatic classification of citation function, in Proceedings of the 2006 conference on empirical methods in natural lan- guage processing (Association for Computational Lin- guistics, 2006) pp. 103–110

    2006

  89. [98]

    H. G. Small, Interpreting maps of science using citation context sentiments: A preliminary investigation, Scien- tometrics 87, 373 (2011)

    2011

  90. [99]

    No¨ e, Cooperation experiments: coordination through communication versus acting apart together, Animal Behaviour 71, 1 (2006)

    R. No¨ e, Cooperation experiments: coordination through communication versus acting apart together, Animal Behaviour 71, 1 (2006)

    2006

  91. [100]

    Adams, Collaborations: the rise of research networks, Nature 490, 335 (2012)

    J. Adams, Collaborations: the rise of research networks, Nature 490, 335 (2012)

    2012

  92. [101]

    Wuchty, B

    S. Wuchty, B. F. Jones, and B. Uzzi, The increasing dominance of teams in production of knowledge, Science 316, 1036 (2007)

    2007

  93. [102]

    Laudel, What do we measure by co-authorships?, Research Evaluation 11, 3 (2002)

    G. Laudel, What do we measure by co-authorships?, Research Evaluation 11, 3 (2002)

    2002

  94. [103]

    Fleming, S

    L. Fleming, S. Mingo, and D. Chen, Collaborative bro- kerage, generative creativity, and creative success, Ad- ministrative Science Quarterly 52, 443 (2007)

    2007

  95. [104]

    Franceschet and A

    M. Franceschet and A. Costantini, The effect of scholar collaboration on impact and quality of academic papers, Journal of Informetrics 4, 540 (2010)

    2010

  96. [105]

    Kawakatsu, T

    M. Kawakatsu, T. A. Kessinger, and J. B. Plotkin, A mechanistic model of gossip, reputations, and cooper- ation, Proc. Natl. Acad. Sci. USA 121, e2400689121 (2024)

    2024

  97. [106]

    Chakrabarti, Status and development: How social hi- erarchy undermines well-being, The Russell Sage Foun- dation Journal of the Social Sciences 8 (2022)

    P. Chakrabarti, Status and development: How social hi- erarchy undermines well-being, The Russell Sage Foun- dation Journal of the Social Sciences 8 (2022). 17

    2022

  98. [107]

    A. M. Petersen, S. Fortunato, R. K. Pan, K. Kaski, O. Penner, A. Rungi, M. Riccaboni, H. E. Stanley, and F. Pammolli, Reputation and impact in academic ca- reers, Proc. Natl. Acad. Sci. U.S.A. 111, 15316 (2014)

    2014

  99. [108]

    A. V. Jaeggi, K. J. Boose, F. J. White, and M. Gur- ven, Obstacles and catalysts of cooperation in humans, bonobos, and chimpanzees: behavioural reaction norms can help explain variation in sex roles, inequality, war and peace, Behaviour 153, 1015 (2016)

    2016

  100. [109]

    C. R. von Rueden, D. Redhead, R. O’Gorman, H. Ka- plan, and M. Gurven, The dynamics of men’s coopera- tion and social status in a small-scale society, Proc. R. Soc. B. 286, 20191367 (2019)

    2019

  101. [110]

    Hyland, Academic attribution: citation and the con- struction of disciplinary knowledge, Applied Linguistics 20, 341 (1999)

    K. Hyland, Academic attribution: citation and the con- struction of disciplinary knowledge, Applied Linguistics 20, 341 (1999)

    1999

  102. [111]

    Thompson and C

    P. Thompson and C. Tribble, Looking at citations: Us- ing corpora in English for academic purposes, Language Learning and Technology 5, 91 (2001)

    2001

  103. [112]

    S´ anchez-Gil, J

    S. S´ anchez-Gil, J. Gorraiz, and D. Melero-Fuentes, Ref- erence density trends in the major disciplines, Journal of Informetrics 12, 42 (2018)

    2018

  104. [113]

    Harwood, An interview-based study of the functions of citations in academic writing across two disciplines, Journal of Pragmatics 41, 497 (2009)

    N. Harwood, An interview-based study of the functions of citations in academic writing across two disciplines, Journal of Pragmatics 41, 497 (2009)

    2009

  105. [114]

    Charles, The construction of stance in reporting clauses: A cross-disciplinary study of theses, Applied Linguistics 27, 492 (2006)

    M. Charles, The construction of stance in reporting clauses: A cross-disciplinary study of theses, Applied Linguistics 27, 492 (2006)

    2006

  106. [115]

    Pecorari, Visible and occluded citation features in postgraduate second-language writing, English for Spe- cific Purposes 25, 4 (2006)

    D. Pecorari, Visible and occluded citation features in postgraduate second-language writing, English for Spe- cific Purposes 25, 4 (2006)

    2006

  107. [116]

    Hyland, Activity and evaluation: reporting practices in academic writing, in Academic discourse, edited by J

    K. Hyland, Activity and evaluation: reporting practices in academic writing, in Academic discourse, edited by J. Flowerdew (Longman, London, 2002) pp. 115–130

    2002

  108. [117]

    Lenoir, Instituting science: The cultural production of scientific disciplines (Stanford University Press, 1997) Chap

    T. Lenoir, Instituting science: The cultural production of scientific disciplines (Stanford University Press, 1997) Chap. The Discipline of Nature and the Nature of Dis- ciplines

    1997

  109. [118]

    The shifting sands of medical-scientific publishing: The rise of reviews, in Scientific Publishing Ecosystem , edited by P. B. Joshi, P. P. Churi, and M. Pandey (Springer, 2024)

    2024

  110. [119]

    Kunda, The case for motivated reasoning, Psycholog- ical Bulletin 108, 480 (1990)

    Z. Kunda, The case for motivated reasoning, Psycholog- ical Bulletin 108, 480 (1990)

    1990

  111. [120]

    Stenhouse, T

    N. Stenhouse, T. A. Myers, E. K. Vraga, J. E. Kotcher, L. Beall, and E. W. Maibach, The potential role of ac- tively open-minded thinking in preventing motivated reasoning about controversial science, Journal of Envi- ronmental Psychology 57, 17 (2018)

    2018

  112. [121]

    S. Y. Dolbier, M. C. Dieffenbach, and M. D. Lieber- man, Open-mindedness: An integrative review of inter- ventions, Psychol Rev (2024)

    2024

  113. [122]

    Rozenblit and F

    L. Rozenblit and F. Keil, The misunderstood limits of folk science: An illusion of explanatory depth, Cognitive science 26, 521 (2002)

    2002

  114. [123]

    A. R. Todd and A. D. Galinsky, Perspective-taking as a strategy for improving intergroup relations: Evidence, mechanisms, and qualifications, Social and Personality Psychology Compass 8, 374 (2014)

    2014

  115. [124]

    Dweck, Implicit theories, in Handbook of theories of social psychology , Vol

    C. Dweck, Implicit theories, in Handbook of theories of social psychology , Vol. 2, edited by P. A. Van Lange, A. W. Kruglanski, and E. T. Higgins (SAGE Publica- tions Ltd, 2012) pp. 43–61

    2012

  116. [125]

    Confraria, T

    H. Confraria, T. Ciarli, and E. Noyons, Countries’ re- search priorities in relation to the sustainable develop- ment goals, Research Policy 53, 104950 (2024)

    2024

  117. [126]

    O. H. Petersen, Inequality of research funding between different countries and regions is a serious problem for global science, Function 2, zqab060 (2021)

    2021

  118. [127]

    Kozlowski, V

    D. Kozlowski, V. Larivi` ere, C. R. Sugimoto, and T. Monroe-White, Intersectional inequalities in science, Proceedings of the National Academy of Sciences of the United States of America 119, e2113067119 (2022)

    2022

  119. [128]

    Mongeon, C

    P. Mongeon, C. Brodeur, C. Beaudry, and V. Larivi` ere, Concentration of research funding leads to decreas- ing marginal returns, Research Evaluation 25, 396–404 (2016)

    2016

  120. [129]

    Hofstra, V

    B. Hofstra, V. V. Kulkarni, S. M.-N. Galvez, B. He, D. Jurafsky, and D. A. McFarland, The diversity- innovation paradox in science, Proc Natl Acad Sci U S A 117, 9284 (202)

  121. [130]

    Leaper and C

    C. Leaper and C. K. Friedman, The socialization of gen- der, in Handbook of socialization: Theory and research , edited by J. E. Grusec and P. D. Hastings (2007) pp. 561–587

    2007

  122. [131]

    Coates, Women, Men and Language: A Sociolinguis- tic Account of Gender Differences in Language (Rout- ledge, 2015)

    J. Coates, Women, Men and Language: A Sociolinguis- tic Account of Gender Differences in Language (Rout- ledge, 2015)

    2015

  123. [132]

    J. S. Hyde, Gender similarities and differences, Annual Review of Psychology 65, 373 (2014)

    2014

  124. [133]

    J. M. DeJesus, V. A. Umscheid, and S. A. Gelman, When gender matters in scientific communication: The role of generic language, Sex Roles 85, 577–586 (2021)

    2021

  125. [134]

    neurosciences

    to self-promotion practices [135], self-citation [136], positive presentation of their own results [137], and use generic statements, framing an idea as broad, timeless, and universally true [133]. Here we demonstrate that those gender differences also extend to the sentiment ...

    1998

  126. [135]

    L. Kim, D. S. Smith, B. Hofstra, and D. A. McFarland, Gendered knowledge in fields and academic careers, Re- search Policy 51, 104411 (2022)

    2022

  127. [136]

    H. Peng, M. Teplitskiy, D. M. Romero, and E.- ´A. Horv´ at, The gender gap in scholarly self- promotion on social media, arXiv 2206, 05330 (2023), arXiv:2206.05330 [cs.DL]

    2023 arXiv

  128. [137]

    M. M. King, C. T. Bergstrom, S. J. Correll, J. Jacquet, and J. D. West, Men set their own cites high: Gender and self-citation across fields and over time, Socius 3, 1–22 (2017)

    2017

  129. [138]

    M. J. Lerchenmueller, O. Sorenson, and A. B. Jena, Gender differences in how scientists present the impor- tance of their research: observational study, bmj 367, 1–8 (2019)

    2019

  130. [139]

    J. J. M. Massen, L. Bauer, B. Spurny, T. Bugnyar, and M. E. Kret, Sharing of science is most likely among male scientists, Scientific Reports 7 (2017)

    2017

  131. [140]

    J. M. Zhu, A. P. Pelullo, S. Hassan, L. Siderowf, R. M. Merchant, and R. M. Werner, Gender differ- ences in twitter use and influence among health policy and health services researchers, JAMA Intern Med 179, 1726 (2019)

    2019

  132. [141]

    Leaper and M

    C. Leaper and M. M. Ayres, A meta-analytic review of gender variations in adults’ language use: Talkativeness, affiliative speech, and assertive speech, Personality and Social Psychology Review 11, 328–363 (2007)

    2007

  133. [142]

    Liu, Many facets of sentiment analysis, in A Practi- cal Guide to Sentiment Analysis , edited by E

    B. Liu, Many facets of sentiment analysis, in A Practi- cal Guide to Sentiment Analysis , edited by E. Cambria, D. Das, S. Bandyopadhyay, and A. Feraco (Springer, 2017)

    2017

  134. [143]

    E. Yan, Z. Chen, and K. Li, The relationship between journal citation impact and citation sentiment: A study 18 of 32 million citances in PubMed Central, Quantitative Science Studies 1, 664 (2020)

    2020

  135. [144]

    M. C. Hall, Obscured social construction as epistemic harm, J Soc Philos 48, 344 (2017)

    2017

  136. [145]

    PMC Open Access Subset [Internet] (2024), Accessed: February 23, 2024

    2024

  137. [146]

    Gender-API, Gender-API (2024), accessed: 2024-06-06

    2024

  138. [147]

    Zhang, Y

    W. Zhang, Y. Deng, B. Liu, S. Pan, and L. Bing, Sen- timent analysis in the era of large language models: A reality check, in Findings of the Association for Compu- tational Linguistics: NAACL 2024 , edited by K. Duh, H. Gomez, and S. Bethard (Association for Computa- tional L...

    2024

  139. [148]

    Tom´ as, R

    D. Tom´ as, R. Ortega-Bueno, G. Zhang, P. Rosso, and R. Schifanella, Transformer-based models for multi- modal irony detection, Journal of Ambient Intelligence and Humanized Computing 14, 7399 (2023)

    2023

  140. [149]

    Carneros-Prado, L

    D. Carneros-Prado, L. Villa, E. Johnson, C. C. Do- brescu, A. Barrag´ an, and B. Garc ´ ıa-Mart ´ ınez, Compar- ative study of large language models as emotion and sentiment analysis systems: A case-specific analysis of GPT vs. IBM Watson, in Proceedings of the 15th Inter- nat...

    2023

  141. [150]

    Reichman, K

    B. Reichman, K. Talamadupula, T. Jawale, and L. Heck, Reading with intent (2024), arXiv:2408.11189

    2024 arXiv

  142. [151]

    Lin and H

    G.-T. Lin and H. yi Lee, Can LLMs understand the im- plication of emphasized sentences in dialogue? (2024), arXiv:2406.11065

    2024 arXiv

  143. [152]

    Catalini, N

    C. Catalini, N. Lacetera, and A. Oettl, The incidence and role of negative citations in science, Proceedings of the National Academy of Sciences 112, 13823 (2015), https://www.pnas.org/doi/pdf/10.1073/pnas.1502280112

    2015 doi

  144. [153]

    Bordignon, Self-correction of science: A comparative study of negative citations and post-publication peer review, Scientometrics 124, 1225 (2020)

    F. Bordignon, Self-correction of science: A comparative study of negative citations and post-publication peer review, Scientometrics 124, 1225 (2020)

    2020

  145. [154]

    W. S. Lamers, K. Boyack, V. Larivi` ere, C. R. Sugi- moto, N. J. van Eck, L. Waltman, and D. Murray, Meta- research: Investigating disagreement in the scientific lit- erature, Elife 10, e72737 (2021)

    2021

  146. [155]

    Achakulvisut, D

    T. Achakulvisut, D. Acuna, and K. Kording, Pubmed parser: A python parser for pubmed open-access xml subset and medline xml dataset xml dataset, Journal of Open Source Software 5, 1979 (2020)

    2020

  147. [156]

    S. M. Mitchell, S. Lange, and H. Brus, Gendered cita- tion patterns in international relations journals, Inter- national Studies Perspectives 14, 485 (2013)

    2013

  148. [157]

    M. L. Dion, J. L. Sumner, and S. M. Mitchell, Gen- dered citation patterns across political science and so- cial science methodology fields, Political Analysis 26, 312 (2018)

    2018

  149. [158]

    Maliniak, R

    D. Maliniak, R. Powers, and B. F. Walter, The gen- der citation gap in international relations, International Organization 67, 889 (2013)

    2013

  150. [159]

    J. D. Dworkin, K. A. Linn, E. G. Teich, P. Zurn, R. T. Shinohara, and D. S. Bassett, The extent and drivers of gender imbalance in neuroscience refer- ence lists, bioRxiv 10.1101/2020.01.03.894378 (2020), https://www.biorxiv.org/content/early/2020/01/11/2020.01.03.894378.full.pdf

    2020 doi

  151. [160]

    M. A. Bertolero, J. D. Dworkin, S. U. David, C. L. Lloreda, P. Srivastava, J. Stiso, D. Zhou, K. Dzirasa, D. A. Fair, A. N. Kaczkurkin, B. J. Marlin, D. Shohamy, L. Q. Uddin, P. Zurn, and D. S. Bassett, Racial and ethnic imbalance in neuroscience reference lists and in- tersec...

    2020

  152. [161]

    X. Wang, J. D. Dworkin, D. Zhou, J. Stiso, E. B. Falk, D. S. Bassett, P. Zurn, and D. M. Lydon-Staley, Gen- dered citation practices in the field of communication, Annals of the International Communication Association 10.1080/23808985.2021.1960180 (2021)

    2021

  153. [162]

    J. M. Fulvio, I. Akinnola, and B. R. Postle, Gender (im)balance in citation practices in cognitive neuro- science, J Cogn Neurosci 33, 3 (2021)

    2021

  154. [163]

    D. Zhou, E. J. Cornblath, J. Stiso, E. G. Teich, J. D. Dworkin, A. S. Blevins, and D. S. Bassett, Gender di- versity statement and code notebook v1.0 (2020)

    2020

  155. [164]

    Ambekar, C

    A. Ambekar, C. Ward, J. Mohammed, S. Male, and S. Skiena, Name-ethnicity classification from open sources, in Proceedings of the 15th ACM SIGKDD inter- national conference on Knowledge Discovery and Data Mining (2009) pp. 49–58

    2009

  156. [165]

    Sood and S

    G. Sood and S. Laohaprapanon, Predicting race and ethnicity from the sequence of characters in a name, arXiv preprint arXiv:1805.02109 (2018)

    2018 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.