The Impact of Informal Mentorship in Academic Collaborations

Bedoor AlShebli; Kinga Makovi; Talal Rahwan

REVIEW 3 major objections 3 minor 52 references

The Impact of Informal Mentorship in Academic Collaborations

T0 review · 3 major / 3 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Informal mentorship quality causally raises a junior scientist's later citation impact, by up to 36 percent, and the size depends on mentor count, mentor age, and the genders of both partners.

desk verdict The paper's own supplementary tables show the mentorship effect running the wrong way, so the headline causal claim is not supported as written; fixable but not acceptable. read the letter →

arxiv 1908.03813 v1 pith:5S3IVHYL submitted 2019-08-10 cs.SI

Bedoor AlShebli , Kinga Makovi , Talal Rahwan This is my paper · ORCID

classification cs.SI

keywords informalmentorshipscientificimpactcoarsenedexactmatchingbig-shotexperiencecitationanalysisgendergapsinsciencementor-protegepairscausalinference

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

This paper tries to show that informal mentorship in academic collaborations—a junior scientist being supported by several senior coauthors without formal supervisory ties—has a causal effect on how much impact the junior scientist has later, after the mentorship ends. Mentorship quality is measured by the mentors' prior citation track record (the 'big-shot experience') and by their centrality in the collaboration network (the 'hub experience'). Comparing matched proteges with coarsened exact matching across 2.5 million mentor-protege pairs, the paper estimates that being mentored by higher-impact senior scientists raises a protege's post-mentorship citation impact by up to 36 percent, with the size growing with the number of mentors and peaking when mentors have about 30 years of experience. It also claims that a higher proportion of female mentors is associated with lower post-mentorship impact, and that female mentors lose about 18 percent of citations when mentoring female rather than male proteges, suggesting current female-female mentorship policies may trade retention for impact. A sympathetic reader would care because it locates an actionable lever—choice of informal mentors—on a measurable career outcome, and it complicates gender-based mentorship policies.

What carries the argument

The engine of the analysis is a pair of matched comparisons built with coarsened exact matching (CEM), a method that selects control proteges who resemble treated proteges on the number of mentors, first mentored-paper year, discipline, gender, affiliation rank, post-mentorship active years, and average mentor age. The treatment is 'big-shot experience'—the average annual citations, up to the first mentorship publication, of all a protege's informal mentors—or, in the secondary analysis, 'hub experience,' the average collaborator-network degree of those mentors. The outcome is 'c5,' the citations a protege's own post-mentorship papers (written without mentors) accumulate five years after publication. CEM is what lets the authors call the big-shot effect causal rather than merely correlational: it is the device that attempts to rule out differences in who gets high-impact mentors.

What would settle it

A concrete check: re-run the matched comparison but add each protege's own citation count—or publication record—before the mentorship period as an additional exact-matching variable or regression control inside the matched sample. If the big-shot effect shrinks toward zero or reverses sign, the causal claim fails, because the apparent effect would then be explained by pre-existing differences among proteges rather than by the mentorship itself.

Watch

Extended reading notes

Core claim

The paper's central claim is that mentorship quality has a causal effect on the scientific impact of the papers a protege writes after the mentorship period ends. The strongest quantified form is the 'big-shot effect': moving a protege from one quintile of mentor prior-impact to the next increases post-mentorship impact by up to 36 percent, whereas the corresponding 'hub effect' from mentors' network centrality never exceeds 7 percent. The effect persists across disciplines, university ranks, and protege gender; it grows with the number of mentors and roughly doubles in recent decades, and it rises with mentors' academic age until around 30 years of experience, then declines. On gender, the paper claims that increasing the proportion of female mentors among a protege's informal mentors decreases the protege's later impact (by up to 35 percent in the matched comparisons), and that female mentors gain on average 18 percent fewer citations from mentoring female than male proteges, while male mentors' gains are unaffected. The paper reads these results as evidence that policies pushing female-female mentorships, however effective at retaining women in science, may reduce the later impact of the women who stay, and that opposite-gender mentorships should be encouraged instead.

Load-bearing premise

The estimate assumes that after matching on the listed covariates, proteges with high-impact mentors and proteges with lower-impact mentors differ only in their mentorship quality—that is, there is no unmeasured difference in the protege's own ability, prior publication record, or selection into high-status mentors that drives both who mentors them and their later impact.

Editorial extensions

If this is right

If mentorship quality is causal, then the impact of a young scientist's later independent work can be raised by changing who they collaborate with early, not just by changing their own effort or resources.
Because the big-shot effect grows with the number of mentors and is strongest with more than five mentors, supporting early-career scientists in building a broad set of senior collaborators is a plausible route to higher impact.
The peak at roughly 30 years of mentor experience means that the most productive informal mentoring may come from mid-to-late-career scientists, not the very newest or the most senior.
The gender results imply that policies designed to retain women by pairing them with female mentors may need to weigh a trade-off: retention gains against a measured reduction in later citation impact for those who stay, and against lower citation gains for female mentors themselves.
Because the hub effect is small relative to the big-shot effect, a mentor's standing in the collaboration network matters less than their demonstrated citation impact—so mentorship policy should prioritize research eminence over connectedness.

Reading between the lines

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

Beyond the paper: a test restricting to same-subfield mentorships would discriminate knowledge transfer from a pure status halo, since the paper does not separate these channels.
Beyond the paper: adding each protege's own pre-mentorship citation trajectory as a matching variable would likely shrink the 36 percent figure, because the paper's match does not include prior output.
Beyond the paper: the gender findings imply that informal mentorship networks are a resource distributed unequally by gender, so opposite-gender mentorship policies could work only if enough high-impact male mentors are available and willing.
Beyond the paper: on the mentor side, the lower gain female mentors experience from female proteges could be driven by audience citation bias rather than mentorship quality, a channel the paper does not distinguish.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Short version: this paper takes an important question—does informal mentorship from high-status senior coauthors move a junior scientist's later citation impact—and goes after it with an enormous MAG dataset and a sensible CEM design. The operationalization of informal mentorship via coauthorship is genuinely new, and the separate analysis of protege outcomes and mentor gains, split by gender, has clear policy relevance. The methods section is detailed and the reference list is appropriate.

The problem is that the headline result is contradicted by the paper's own supplementary tables. In Tables S3–S10, C′ and T′ are the matched lower- and higher-mentorship-quality groups, and imp(C′) and imp(T′) are their mean post-mentorship impacts. In every single row, imp(C′) > imp(T′). For example, Q1 vs. Q2 shows 14.86 vs. 11.66, and Q4 vs. Q5 shows 28.26 vs. 20.86. Yet the reported δ is positive throughout. The only way to produce those positive values is to use the inverted formula, 100·(imp(C′) − imp(T′))/imp(T′), instead of the stated 100·(imp(T′) − imp(C′))/imp(C′). The same inversion appears in the hub-effect table and in all of the stratified big-shot tables. So the paper's own matched means say that higher big-shot mentorship is associated with lower post-mentorship impact, not a 36% benefit.

This is not a minor numerical typo. It reverses the central causal claim in the abstract and in Figures 1–2. There are also the usual observational concerns—no sensitivity analysis, no control for the protege's own prior publication record, so the unconfoundedness assumption is doing heavy lifting. But even if unconfoundedness held, the sign is backwards.

To be fair, the data construction looks careful and the matching design is reasonable in principle. The gender-specific findings might survive re-analysis. But as submitted the paper cannot support its stated conclusions. I would not desk-reject-and-forget: the question and dataset are worth serious attention, and this looks fixable. But I would not accept it, and I would want the authors to correct the arithmetic and re-run before any referee spends time on it.

Bottom line: important question, real dataset, decisive internal inconsistency. Once corrected, it matters for science-of-science and gender-equity policy readers; don't cite the current version.

Referee Report

3 major / 3 minor

Summary. The paper investigates whether informal mentorship by senior coauthors affects junior scientists' later independent impact. Using Microsoft Academic Graph data, the authors define protégé-mentor pairs, measure mentorship quality by mentors' prior citation impact ("big-shot experience") and prior collaboration degree ("hub experience"), and use coarsened exact matching between adjacent quintiles of these measures to estimate effects on the protégé's average five-year citation impact after mentorship. The paper reports positive causal effects (up to 36% for high-status mentors), increasing with mentor count and with mentor age up to a threshold, and analyzes gender composition effects on both protégé outcomes and mentors' citation gains.

Significance. The research question is important, and the dataset is unusually large. I credit the authors for making their matching definitions and supplementary tables explicit; this transparency is what exposes the internal inconsistency discussed below. Nevertheless, the paper's primary evidence contradicts its headline claim: in the main matched comparison, higher mentorship-quality groups have lower post-mentorship impact than controls. Since this reversal affects the abstract, Results, and the policy-related gender conclusions, the significance of the claimed causal effect cannot be credited as presented.

major comments (3)

[Supplementary Table S3] The definition of δ in Supplementary Note 3 is δ = 100·(imp(T′) − imp(C′))/imp(C′), with C = Qi and T = Qi+1. For Q1 vs Q2, the table reports imp(C′) = 14.86 and imp(T′) = 11.66, which gives δ = −21.5%, not +27.4%. The same reversal occurs in every row: Q2 vs Q3 gives −14.1% (reported +16.4%), Q3 vs Q4 gives −17.7% (reported +21.5%), and Q4 vs Q5 gives −26.2% (reported +35.5%). The reported positive values are those that would be obtained from 100·(imp(C′) − imp(T′))/imp(T′), i.e., both the sign and the denominator are inverted. This is not a local typo: it reverses the direction of the headline causal effect.
[Supplementary Tables S4–S10] The same pattern is systematic rather than isolated. In Table S4 (hub effect), each higher-quintile matched group has lower mean post-mentorship impact than the lower-quintile group (e.g., 18.61 vs 19.83 for Q1 vs Q2, reported as +6.6% instead of −6.2%), and the same inversion recurs in the large majority of stratified rows in Tables S5–S10 for time periods, mentor-age bins, mentor-count bins, university-rank bins, gender, and discipline. Some individual rows, such as Geology Q2 vs Q3 in Table S10, do report negative δ correctly, which shows that the issue is not a uniformly applied alternative convention. Because the matched control group consistently outperforms the treatment group, the abstract's central claim that higher mentorship quality increases post-mentorship impact is not supported by the paper's own evidence; the observed matched association is negative.
[Results: Causal identification] Even if the sign issue were corrected, the causal claim rests on an unconfoundedness assumption that is asserted rather than supported. The matching variables listed where CEM is introduced (number of mentors, first mentored-paper year, discipline, gender, affiliation rank, post-mentorship active years, average mentor age, and the other mentorship-quality measure) do not include the protégé's own prior productivity or prior citation impact, and no sensitivity analysis or placebo test is provided. Because both the treatment (mentors' prior citation impact) and the outcome (protégé's post-mentorship citation impact) are citation-based measures, selection of higher-ability protégés into mentors with higher citation impact is a concrete alternative explanation that the current design does not rule out.

minor comments (3)

[Supplementary Table S7] The rows for two mentors and three mentors are numerically identical, which appears to be a copy-paste error and should be corrected.
[Figure 1 caption] The caption says to see Supplementary Tables S1 and S2 for details, but the actual CEM result tables for the big-shot and hub effects are Supplementary Tables S3 and S4.
[Reference [20]] Reference [20] credits Fortunato et al. with the title "Hot streaks in artistic, cultural, and scientific careers"; the correct title of that review article is "Science of science". The hot streaks paper is already reference [17].

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the treatment and outcome are independently measured citation aggregates, and the causal claim rests on CEM comparisons rather than on definitional identity or a self-citation chain.

full rationale

The paper's central claim is empirical: mentorship quality, measured by mentors' average annual citations before mentorship ('big-shot experience') or average collaboration degree ('hub experience'), is compared against the protege's later citation impact on papers written without those mentors (c5). These constructs are not defined in terms of one another; the outcome excludes co-authored mentor papers, while the treatment is computed from mentor histories prior to the collaboration. No parameter is fitted to the outcome and then renamed as a prediction; the reported delta is a direct relative difference between matched group means using the formula in Supplementary Note 3. The CEM design is standard and the confounding controls are listed explicitly. The only self-citation is to the authors' own prior work [18] for the scientist-discipline classification method; this is a methodological input, not a uniqueness theorem or an argument that forces the causal conclusion, so it is not load-bearing in the circularity sense. A separate concern, visible in Supplementary Tables S3-S10, is that the printed imp(C') and imp(T') values are everywhere inconsistent with the printed positive delta values, which would imply a sign-reversal or reporting error. That is an internal-consistency or correctness problem, not a circularity: even if the arithmetic were wrong, the treatment and outcome would still be independently defined. No reduction of the paper's derivation to its own inputs was found, so the circularity score is 0.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new physical entities, forces, or particles. Its invented constructs are operational variables such as big-shot experience and hub experience, which are defined directly from citation and collaboration data rather than postulated independently. The main dependencies are hand-chosen thresholds and a set of domain assumptions about what coauthorship means and what observables are sufficient for causal inference.

free parameters (4)

junior_career_threshold = 7 years
The junior/senior split is set at 7 years since first publication. This hand-chosen threshold defines who counts as a protege and who counts as a mentor.
c5_measurement_window = 5 years
Outcome impact is measured as citations accumulated 5 years after publication. This window is chosen by the authors and also determines which recent papers can enter the sample.
publication_gap_threshold = 5 years
Proteges with a publication gap of 5 years or more are excluded from the analysis. This threshold affects which scientists are considered continuously active.
gender_confidence_threshold = 95 percent
The authors keep only proteges and mentors whose gender is predicted with at least 95 percent certainty by genderize.io. This choice changes the sample composition.

assumptions (6)

domain assumption Microsoft Academic Graph accurately records authors, affiliations, publication dates, and citations.
The entire analysis depends on MAG data being complete and correct over a century of publications.
domain assumption Citation count five years after publication is a valid measure of scientific impact.
The outcome variable is c5, so the paper's conclusions about mentorship quality are only as strong as this proxy for impact.
domain assumption Coauthorship between a junior and a senior scientist at the same US institution constitutes informal mentorship.
The paper equates any qualifying coauthorship with a mentorship relationship, which is a strong modeling assumption about the meaning of collaboration.
domain assumption Academic age, measured as years since first publication, accurately reflects career stage.
All junior/senior classifications and mentorship-period definitions rest on this assumption.
domain assumption Coarsened exact matching on the listed observed confounders identifies average causal effects.
The paper claims causality from an observational matching design. This requires an unconfoundedness assumption that is stated implicitly and not tested.
domain assumption Gender inferred from first names via genderize.io is accurate for the retained sample.
The gender analyses depend entirely on this external name-based classification, subject to accuracy and sample selection issues.

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Cite this review

Pith. "Pith review of The Impact of Informal Mentorship in Academic Collaborations." pith.science (2026). https://pith.science/paper/5S3IVHYL

@misc{pith2026190803813,
  author       = {Pith},
  title        = {Pith review of: The Impact of Informal Mentorship in Academic Collaborations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5S3IVHYL}},
  note         = {Machine review of arXiv:1908.03813}
}

read the original abstract

Inspired by the numerous benefits of mentorship in academia, we study "informal mentorship" in scientific collaborations, whereby a junior scientist is supported by multiple senior collaborators, without them necessarily having any formal supervisory roles. To this end, we analyze 2.5 million unique pairs of mentor-prot\'eg\'es spanning 9 disciplines and over a century of research, and we show that mentorship quality has a causal effect on the scientific impact of the papers written by the prot\'eg\'e post mentorship. This effect increases with the number of mentors, and persists over time, across disciplines and university ranks. The effect also increases with the academic age of the mentors until they reach 30 years of experience, after which it starts to decrease. Furthermore, we study how the gender of both the mentors and their prot\'eg\'e affect not only the impact of the prot\'eg\'e post mentorship, but also the citation gain of the mentors during the mentorship experience with their prot\'eg\'e. We find that increasing the proportion of female mentors decreases the impact of the prot\'eg\'e, while also compromising the gain of female mentors. While current policies that have been encouraging junior females to be mentored by senior females have been instrumental in retaining women in science, our findings suggest that the impact of women who remain in academia may increase by encouraging opposite-gender mentorships instead.

Figures

Figures reproduced from arXiv: 1908.03813 by the authors.

**Figure 1.** The big-shot effect and hub effect. For every independent variable, be it big-shot experience or hub experience, Qi denotes the i th quintile of the distribution of that variable. For i ∈ {1, 2, 3, 4}, we consider Qi and Qi+1 to be the control and treatment groups, respectively, and write Qi vs. Qi+1 when referring to the CEM used to compare these two groups. The color of the bar indicates whether the independent va… view at source ↗

**Figure 2.** Trends in big-shot effect. a, Big-shot effect over time. b, Big-shot effect across varying ages of the mentors. c, Big-shot effect across different numbers of mentors. In all subfigures, Qi : i ∈ {1, 2, 3, 4} represents the i th quintile of the distribution of big-shot experience for all proteg´ es that fall in the same ´ bin of either the year of their first publication (subfigure a), the average age of the mentors… view at source ↗

**Figure 3.** The relationship between gender and the gain from mentorship. a, Fi denotes the set of proteg´ es that have exactly ´ i female mentors. Focusing on male proteg´ es, ´ F0 vs. Fi : i = 1, . . . , 5 refers to the change in the post-mentorship impact of proteg´ es in ´ Fi relative to the post-mentorship impact of those in F0 while controlling for the proteg´ e’s big-shot experience, number of mentors, discipline, affili… view at source ↗

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Reference graph

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Academic ranking of world universities. http://www.shanghairanking.com/ARWU2018.html (2018). 8 Methods The data used for this study was obtained in October 2015 from the Microsoft Academic Graph (MAG) dataset [36]. This data set includes records of scientiﬁc publications speci...

2018 arXiv
[41]

The data points are divided into 10 bins based on percentile cutoffs as follows: <4.0; 4.0-7.1; 7.2-10.5; 10.6-14.2; 14.3-18.4; 18.5-23.3, 23.4-29.5; 29.6-38.2; 38.3-54.2;>54.2

Big-Shot experience (IV): This is computed for any given prot´eg´e by ﬁrst computing the average annual number of citations of each mentor up to the year of their ﬁrst publication with the prot ´eg´e, and then averaging these numbers over all mentors. The data points are divid...
[42]

Note that this network changes over time

Hub experience (IV): This is computed for any given prot ´eg´e as the average degree of the mentors prior to mentorship, where the degree of each such mentor is measured in the network of scientiﬁc collaborations, i.e., the network where every node represents a scientist and a...
[43]

Number of mentors (C) : The total number of mentors that the prot ´eg´e has, which is divided into 5 bins based on quintile cutoffs as follows: 1; 2; 3; 4-5;>5
[44]

This is divided into 10 bins based on percentile cutoffs as follows: 4 <1987; 1987-1992; 1993-1996; 1997-1998; 1999-2001; 2002-2003; 2004-2005; 2006- 2007; 2008-2009;≥ 2010

Year of the prot´eg´e’s ﬁrst publication (C): The year in which the prot´eg´e published their ﬁrst mentored paper. This is divided into 10 bins based on percentile cutoffs as follows: 4 <1987; 1987-1992; 1993-1996; 1997-1998; 1999-2001; 2002-2003; 2004-2005; 2006- 2007; 2008-2...

1987
[45]

Gender of the prot ´eg´e (C): The data points in our study are divided into 2 bins: male and female. Following (1),the gender of prot´eg´es and mentors is identiﬁed using a state- of-the art gender classiﬁer (2), and drop all mentor-prot´eg´e pairs where the gender of the prot...
[46]

The data points in our study are divided into the following bins: 1; 2; 3

University rank (C): The rank1 of the afﬁliation of the prot ´eg´e on their ﬁrst mentored publication. The data points in our study are divided into the following bins: 1; 2; 3; . . . ; 99; 100; 101-150; 151-200; 201-300; 301-400; 401-500;>500
[47]

Average academic age of mentors (C): This is computed for any given prot ´eg´e by ﬁrst computing the academic age 2 of each mentor in the year of their ﬁrst publication with the prot´eg´e, and then averaging these numbers over all the mentors. The data points are divided into ...
[48]

Academic Ranking of World Universities

The number of years post mentorship (C) : Since our dataset was obtained in October 2015, we are only able to calculate c5—the number of citations accumulated ﬁve years post publication—for papers published before 2011. Thus, given a prot ´eg´e whose ﬁrst paper was published i...

2015
[49]

Scientiﬁc discipline (C) : The prot ´eg´es are classiﬁed into disciplines using the method proposed by ( 1). This method classiﬁes any given scientist, si, based on the keywords that are speciﬁed in the publications of si, which are themselves classiﬁed by Microsoft Academic G...

1911
[50]

K., Rahwan, T

AlShebli, B. K., Rahwan, T. & Woon, L. W. The preeminence of ethnic diversity in scientiﬁc collaboration. Nature Communications 9, 5163 (2018)

2018
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Gender prediction methods based on ﬁrst names with genderizer

Wais, K. Gender prediction methods based on ﬁrst names with genderizer. The R Journal 8, 17–37 (2016)

2016
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M., King, G

Iacus, S. M., King, G. & Porro, G. Causal inference without balance checking: Coarsened exact matching. Political analysis 20, 1–24 (2012). 16

2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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