How collaboration benefits disruption is widely discussed in academia, but less attention is paid to mentorship in the collaboration of an article. This study focuses on the association between close/open-mentorship measured by whether coauthors in publications belong to the same academic genealogy and the disruption of publications measured by the Disruption Index (DI). We selected 361,189 publications in Neuroscience from the SciSciNet database and then constructed regression models and estimated the relationship between the variables. Moreover, we use Propensity Score Matching and causal forest to estimate the causal relationship between them. The findings show that articles with open-mentorship collaboration are more disruptive than those with close-mentorship collaboration. The findings provide implications for team formation and team management in practice.
In the past decades, scientific papers have become
less disruptive [
To address the question, we first define the term mentorship. Mentorship can occur formally through doctoral and postdoctoral advisor-advisee relationships or informally through collaborations. Some genealogy databases like The Academic Family Tree encompass both advisor-advisee relationships
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and broad range relationships which means the
mentee may be the “learner” in mentoring
relationships regardless of age or position [
We derive mentorship from the dataset released by
Qing et al (2022) [
We adapt Propensity Score Matching (PSM) to
validate causality between mentorship type and DI.
The main effect of this study is the effect of the
closementorship team on DI (treatment effect). The effect
can be influenced by some confounding factors. from
the literature review, team-related, personal-related,
article-related factors can be considered as
confounding factors of DI. Table 1 shows the variables
we included in this study. The variable “outcome” (DI)
is the explained variable. Admittedly, there is a large
bulk of factors that may influence DI, but it is hard to
include all factors. As implemented in previous
studies, we selected those factors for which: (1) prior
work has investigated the factors possibly influencing
DI; (2) existing studies had verified the relationship
with DI; (3) the data for calculating the factors were
available in records from SciSciNet [
To check the robustness of the PSM, we use causal
forest (CF), a state-of-art causal inference method
[
In the causal forest, considering the analysis of heterogeneous causal effects, our estimation objective is Conditional Average Treatment Effects (CATE). The CATE for a given observation is defined as: τ(x) = E[ YiW=1 − YiW=0 ∣∣ Xi = x ] (Eq. 1) where i = 1,2, … , n represents the paper in our sample and Wi ∈ {0,1} indicates whether the team of paper is close-mentorship. We observe the outcome of interest YW=1 if the paper is assigned to the i treatment condition (i.e., if the team of paper is closementorship), otherwise we observe YiW=0. Xi denote a vector of the paper`s other characteristics.
From the data we observed, the number of papers with open-mentorship teams dramatically increased until 2011. However, the number of papers with closementorship teams is 0 after 1980. The number of papers with open-mentorship teams far exceeds that with close-mentorship teams (Figure 1). We tested the between-group difference between the two groups by Mann-Whitney test through Python. The result shows that there is a significant difference between the two groups (p<0.001).
We first answer the question by OLS regression. When only the independent variable relationship was included in the model. The regression coefficient of the variable is negative and significant at the 0.01 level, providing initial evidence that close mentorship has a negative effect on disruption. The magnitude of this coefficient changed significantly when we added the control variables one by one. In the final model, we controlled all the confounding variables and fixed effect, and the model specification had the largest adjusted R2, suggesting that the explanatory power of the model was enhanced by the control. The results in the final model show that a close-mentorship team has a significantly negative effect on disruption。 (a)
We test the relationship between mentorship and DI through PSM. We categorized papers with a closementorship team as the treatment group (29,556 samples) and papers with an open-mentorship team as the control group (331,632 samples). Figure 2(a) shows that the propensity score distributions of the two groups of samples are significantly different, while the propensity scores of the two groups converge after matching. However, after matching the two groups of samples, the distributions of PY, CI, A10, TS, RC, AA, AP, and AC are the same, which indicates that the matching is effective. Through the hypothesis test commonly used in AB experiments, we found that there is a significant difference in DI between the two groups (p<0.05), with the close-mentorship team having an average of -0.002917 DI lower than the open-mentorship team, which means that the DI of the close-mentorship team is 36.34% lower than the DI of the open-mentorship team (Figure 2(b)).
To check the robustness of the results, we used causal forest (CF), a state-of-the-art method. For each paper, we obtain an individualized treatment effect with its 95% confidence interval estimated. The CATEs of the close-mentorship team have a mean of 0.0004. In other words, the close-mentorship team decreases DI by 0.0004 times. However, when we take citation counts as the dependent variable, we found that the CATEs of the close-mentorship team have a mean of 8.503, which means that papers with closementorship may have more citation counts. (a)
This study investigated whether the closementorship team fuses more disruptive ideas than the open-mentorship team. We used academic genealogy to quantify whether an article was close-mentorship or open-mentorship and used the Disruption Index to quantify the disruption idea. We investigated the relationship between the variables by analyzing papers in Neuroscience and constructing regression models. Moreover, we used PSM and causal forest to test whether there is a causal relationship between mentorship type and DI. The results indicate that the articles with the close-mentorship team are less disruptive than those with the open-mentorship team. However, the articles with the close-mentorship team are more cited than those with the open-mentorship team.