{"id":"c338446c-864e-407d-9229-9c05acc60270","arxiv_id":"1908.09120","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"Co-citation, shared-author, and shared-editor networks among journals are significantly correlated in all three fields studied, with the strongest correlation between co-citation and shared-author networks.","lead":"The paper compares three kinds of journal networks, co-citation, interlocking editorship, and interlocking authorship, across statistics, economics, and library science. It finds that journals intellectually close through citations are also close through shared authors and editors, suggesting that intellectual and social proximity in science go together.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The matrix correlations are not tested against degree-preserving nulls, so the headline association between intellectual and social proximity may be an artifact of shared network density and hub structure.","rationale":"I read the paper as an exploratory, descriptive comparison of three journal-level networks. The central quantitative support for the conclusion that intellectual proximity is also proximity among authors and editors is Table 1, where all pairwise generalized distance correlations are around 0.5-0.75 and the permutation tests reject independence. The paper is transparent about data and methods, and the community analysis is explicitly exploratory. The weakest step is the move from 'the matrices are statistically dependent' to 'the social networks tell the same story as the intellectual network.' The independence permutation only breaks the pairing of matrix entries; it preserves the marginal structure of each matrix, including the fact that high-output journals have small Jaccard distances to nearly everyone in every network. A null that preserves degree sequences would show whether the pairwise association in Table 1 is larger than what would arise from the observed densities alone. This is a correctness risk, not an internal inconsistency, and it is testable. I do not see evidence of fraud or carelessness; the issue is an incomplete control, and it is exactly why a conditional verdict is appropriate. Since this matches the reader's weakest assumption and verdict, no adjustment is needed.","tokens_in":9418,"tokens_out":6879,"duration_ms":78900,"concrete_test":"For each field, reconstruct the bipartite incidence matrices (journals x editors, journals x authors, journals x citing articles), then generate 1,000 resampled incidence matrices with the same row and column sums via a bipartite configuration model or stub-matching algorithm that preserves all degrees. Recompute the one-mode Jaccard dissimilarity matrices and the generalized distance correlations for the three pairs within each field. If the observed sqrt(Rd) values lie below the 95th percentile of the resampled distribution, the reported correlations are consistent with degree and density confounding and the central claim should be weakened; if they remain clearly above, the concern is rebutted.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 3, Table 1 reports generalized distance correlations among Jaccard dissimilarity matrices, but the permutation test used there only destroys the pairing between the two matrices; it does not remove the common degree and density structure of the networks being compared. The densities in Table 2 differ by an order of magnitude across network types (e.g., statistics IA density 0.91 versus IE density 0.12), and the Jaccard dissimilarity of a journal to all others is strongly determined by how large that journal's set of authors, editors, or citing articles is. As a result, any two overlap-based dissimilarity matrices with the same high-degree journals will appear correlated even if the actual assignment of people or citations is random given journal size. The paper therefore overstates the support for the claim that intellectual proximity is also a proximity among authors and editors unless this null is ruled out.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper compares, for three fields (statistics, economics, and information and library sciences), three types of journal-level networks: co-citation (CC), interlocking editorship (IE), and interlocking authorship (IA). The authors compute Jaccard dissimilarity matrices for each network, test the association between pairs of matrices using generalized distance correlation with permutation tests, and additionally partition each network into communities with the Louvain algorithm and compare the partitions using chi-square, Cramér's V, Rajski's coherence, and the adjusted Rand index. They report that all three networks are significantly associated in all three fields, with the strongest association between CC and IA, and conclude that intellectual proximity among journals is accompanied by proximity among authors and, more surprisingly, among editors.","tokens_in":9527,"tokens_out":5984,"duration_ms":65717,"significance":"The paper addresses a substantive question about the alignment between intellectual and social structures in scholarly communication, using original data and a comparative design across three fields. Its strengths include the use of generalized distance correlation rather than the classical Mantel test, the combination of whole-network and community-level comparisons, the explicit availability of raw data on Zenodo, and a clearly exploratory framing. If the observed associations survive controls for degree and density, the findings would offer credible evidence that journal gatekeeping, authorship, and intellectual organization are structurally coupled. The main limitation, the absence of degree-preserving null models, currently tempers the strength of the central claim.","major_comments":[{"comment":"The permutation test used for the generalized distance correlation rejects independence, but it does not rule out the alternative that the dissimilarity matrices are correlated because they share a common degree or density gradient. Jaccard dissimilarity is strongly influenced by set sizes, and Table 2 shows that the IA networks are an order of magnitude denser than the IE networks (e.g., statistics IA density 0.91 versus IE density 0.12). A degree-preserving null model, such as randomizing the underlying bipartite graphs while fixing journal degrees before projection, or rewiring the one-mode networks with fixed degree sequences, is needed to benchmark the observed sqrt(R_d) values. Without such a control, the central claim that intellectual proximity is also author and editor proximity is overstated.","section":"Section 3, Table 1"},{"comment":"The resolution parameter 0.8 for the ILS IA network was selected post hoc because it produced better E-I indices. This selection is not accompanied by a stability analysis or a pre-specified criterion, so the community-based association results for that field carry an optimistic bias. The acknowledged instability of community detection in economics similarly weakens the community-level conclusions; a sensitivity analysis over a range of resolution parameters would be needed to establish that the reported associations are not artifacts of the chosen partitions.","section":"Section 4, Table 2 and footnote 1"},{"comment":"The claim that CC-IA is the strongest association is based on point estimates only. For statistics, the CC-IA sqrt(R_d) of 0.6431 is close to the IE-IA value of 0.5985, and no uncertainty intervals or tests of equality of dependent correlations are provided. The ordering of correlation strengths should be treated as descriptive unless accompanied by bootstrap intervals or a formal comparison of dependent correlation coefficients.","section":"Section 5 and Abstract"}],"minor_comments":[{"comment":"The sets A and B are defined explicitly only for the IE network; please specify what the sets represent for the CC and IA networks, namely citing-article sets and author sets, respectively.","section":"Section 3, Eq. (1)"},{"comment":"P-values are reported with inconsistent numbers of decimal places (e.g., 0.00001 versus 0.00058); please use a uniform formatting convention.","section":"Table 1"},{"comment":"The asymmetric Rajski coherence variants ('right' and 'left') are described only verbally; providing the formulas would make the direction of prediction unambiguous and improve reproducibility.","section":"Section 4"},{"comment":"There is a grammatical error in 'The strongest correlations is' which should read 'The strongest correlations are'.","section":"Abstract and Section 5"},{"comment":"The figure captions are minimal; since the visual comparison of networks is part of the exploratory analysis, please indicate the layout algorithm and how edge weights are represented in each figure.","section":"Section 2, Figures 1-9"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should read this paper if you care about the overlap between intellectual and social proximity in journal networks. It does something clean: it constructs interlocking editorship (IE), interlocking authorship (IA), and co-citation (CC) networks for three fields, compares their dissimilarity matrices with generalized distance correlation instead of the usual Mantel test, and reports permutation p-values. Data are deposited, methods are specified, and the comparison to Ni et al. (2013) is honest. The main descriptive result—CC and IA are the most correlated pair in all three fields, IE correlates less—is probably real, but I would not take the magnitudes at face value.\n\nThe soft spot is exactly what the stress-test note flags. The permutation test only breaks the pairing between two dissimilarity matrices; it does not control for the fact that the Jaccard dissimilarity of a journal to all others is heavily determined by the size of its author/editor/cited-article set. Densities in Table 2 differ by an order of magnitude across network types (e.g., statistics IA density 0.91 vs IE density 0.12). A high-degree journal in IA is likely also high-degree in CC because both are driven by journal size. So the correlations in Table 1 could be inflated by a shared size/hub factor rather than by genuine structural similarity. The paper does not run a degree-preserving null (e.g., configuration model) to rule this out. That is a real gap, and it is the main reason the headline claim should be tempered.\n\nA second, minor issue is the post hoc choice of resolution 0.8 for the ILS IA network, chosen because it gave better E-I indices. The authors report it in a footnote, which I appreciate, but it is still a model selection step that can overstate community agreement. The economics community detection is honestly acknowledged as unstable, which is good but also weakens what you can conclude there.\n\nCredit where due: the innovation is not conceptual—Ni et al. already explored the general question—but the systematic three-field comparison and the use of distance correlation are genuinely useful. The paper is empirically grounded, the figures are informative, and the writing is clear. The fact that editorial board data were collected by hand and the raw networks are openly deposited is a real plus; this is reproducible work.\n\nThe take-home: the paper is a solid extension, not a breakthrough. Its main substantive claim—intellectual proximity aligns with author and editor proximity—likely holds, but the current analysis does not fully exclude a density/size artifact. A serious referee should ask the authors to run degree-preserving nulls and to justify or robustness-check the post hoc resolution. That is fixable, and the paper deserves referee time rather than desk rejection.\n\nI would bring it to a reading group focused on scientometrics or network methods, and I would probably cite it as the most systematic empirical comparison of these three network types. My verdict: conditional acceptance after the missing null is addressed.","headline":"Solid empirical extension with deposited data, but the headline correlation claim needs a degree-preserving null before it can be fully trusted.","tokens_in":10030,"tokens_out":1005,"would_cite":true,"duration_ms":12897,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper shows that a journal's intellectual neighbours are also its author and editor neighbours, and that the co-citation, authorship, and editorial-board networks are positively associated in all three fields studied.","keywords":["interlocking editorship","interlocking authorship","co-citation networks","journal networks","generalized distance correlation","community detection","scholarly communication","scientific gatekeeping"],"falsifier":"Rewire each network randomly while preserving every journal's degree, then recompute the generalized distance correlations. If values such as $\\sqrt{R_d}=0.75$ for economics co-citation versus authorship appear as often in the rewired networks as in the real data, the reported similarity between intellectual and social maps would be refuted as an artifact of network density.","tokens_in":9216,"feed_emoji":"📚","tokens_out":7259,"duration_ms":70746,"temperature":0.7,"pith_summary":"The paper asks whether the intellectual map of a research field—which journals sit close together because they are cited together—matches the social map of who writes for and governs the journals. It builds three networks for each of statistics, economics, and information and library sciences: co-citation, interlocking authorship, and interlocking editorship. All three networks are positively associated in all three fields, with co-citation and interlocking authorship the closest pair and co-citation and editorship the weakest. The communities detected in each network line up more than independence would predict. The authors conclude that intellectual proximity is also social proximity, so the maps of editorial power, intellectual proximity, and author communities tell similar stories.","feed_headline":"Co-citation, author, and editor maps align in three fields","feed_subtitle":"Across statistics, economics, and library science, who cites, writes, and edits journals line up more than chance.","key_machinery":"The comparison rests on three one-mode journal networks per field: nodes are journals, and an edge between two journals means they share at least one editor (interlocking editorship, IE), share at least one author (interlocking authorship, IA), or are cited together (co-citation, CC). Each network is converted into a matrix of pairwise Jaccard dissimilarities, and the matrices are compared with the generalized distance correlation ($\\sqrt{R_d}$), a matrix-level analogue of a correlation coefficient with a permutation test for independence. The networks are then partitioned into communities by a modularity-optimising algorithm, and the resulting partitions are compared with Cramér's $V$, Rajski's coherence, and the adjusted Rand index. The recurring ordering of associations—co-citation with authorship strongest, co-citation with editorship weakest—is the pattern carrying the argument.","core_discovery":"On the paper's own terms, the discovery is that the intellectual proximity encoded by co-citation is mirrored, to a statistically detectable degree, by the social proximity encoded by shared authors—and, more surprisingly, by shared editors. Across statistics, economics, and information and library sciences, the three distance matrices correlate above $0.5$ in every pairwise comparison; the strongest correlations are between co-citation and interlocking authorship ($\\sqrt{R_d}=0.64$, $0.64$, and $0.75$ for the three fields). Communities detected in each network are not independent, and again the co-citation/author association is the strongest while co-citation/editor is the weakest. The paper reads these results as evidence that the three maps—editorial power, intellectual proximity, and author communities—are coherent across scholarly fields.","pith_inferences":["A testable extension would be to track editor appointments over time: if incoming editors begin publishing in their new journals, board overlap may partly cause author overlap rather than merely correlate with it.","A stronger control than the paper's permutation test would be degree-preserving rewiring of each network; if the observed correlations survive that, the claim of substantive intellectual-social proximity would be on firmer ground.","Comparing fields where editors rarely publish in the journals they oversee would reveal how much of the coherence is driven by the same people appearing as authors and as editors."],"forward_implications":["In any field with sufficient data, the same three-map coherence should appear; the paper explicitly argues the method is generally applicable beyond statistics, economics, and information and library sciences.","Journal rankings and bibliometric indicators cannot be treated as purely intellectual measurements, because they are entangled with who publishes in and who edits the journals.","Because the strongest association is between co-citation and interlocking authorship, scholars' choices about where to publish are the social layer most tightly coupled to intellectual content.","Editorial-board networks are more loosely tied to the other two but still predict them, suggesting gatekeeping positions and intellectual clusters are connected even when the connection is weaker."],"supporting_citations":[{"why":"Defines co-citation as the paper's measure of intellectual similarity between journals.","marker":"Small (1973)"},{"why":"Provides the statistics editorial-board dataset and the interlocking-editorship network construction.","marker":"Baccini, Barabesi, and Marcheselli (2009)"},{"why":"Provides the economics editorial-board dataset used for the IE network.","marker":"Baccini and Barabesi (2010)"},{"why":"Provides the information and library sciences editorial-board dataset used for the IE network.","marker":"Baccini and Barabesi (2011)"},{"why":"Supplies the generalized distance correlation and the permutation independence test used for the Table 1 comparisons.","marker":"Omelka and Hudecová (2013)"},{"why":"Introduces distance correlation, the statistical foundation the generalized version builds on.","marker":"Székely et al. (2007)"},{"why":"Supplies the modularity-optimising algorithm used to partition each network into communities.","marker":"Blondel et al. (2008)"},{"why":"Defines modularity, the objective function that the community detection optimises.","marker":"Newman and Girvan (2004)"},{"why":"Is the earlier comparable study on information and library sciences whose results the paper says are coherent with its own.","marker":"Ni, Sugimoto, and Cronin (2013)"}],"fun_headline_variants":["Journal proximity: citations, authors, editors tell same story","Intellectual and social maps of journals align across fields","Co-citation, authorship, editorship: three networks, one picture","Social and intellectual closeness of journals go hand in hand"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The claim rests on the assumption that the measured correlations reflect genuine overlap in scholarly communities rather than an artifact of how dense each network happens to be; if density or shared degree structure alone produces the correlations, the similar maps could be coincidental.","fun_headline_variants_meta":{"raw":{"variants":["Journal proximity: citations, authors, editors tell same story","Intellectual and social maps of journals align across fields","Co-citation, authorship, editorship: three networks, one picture","Social and intellectual closeness of journals go hand in hand"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000622,"raw_usage":{"total_tokens":2903,"prompt_tokens":987,"completion_tokens":1916,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":603,"completion_tokens_details":{"reasoning_tokens":1848}},"tokens_in":603,"tokens_out":1916,"duration_ms":15558,"temperature":1.0,"reasoning_tokens":1848,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-14T11:20:30.338914+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Rewire each network randomly while preserving every journal's degree, then recompute the generalized distance correlations. If values such as $\\sqrt{R_d}=0.75$ for economics co-citation versus authorship appear as often in the rewired networks as in the real data, the reported similarity between intellectual and social maps would be refuted as an artifact of network density.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines co-citation as the paper's measure of intellectual similarity between journals."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the economics editorial-board dataset used for the IE network."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the information and library sciences editorial-board dataset used for the IE network."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the generalized distance correlation and the permutation independence test used for the Table 1 comparisons."},{"cited_title":"D., J.-L","cited_arxiv_id":null,"evidence_quote":"Supplies the modularity-optimising algorithm used to partition each network into communities."}],"review_version":1}