The Impact of Mentorship on the Research Performance of LIS PhDs
Bibliographic record
Abstract
The findings of our study suggest that the doctoral mentorship relationship may play a significant role in student research performance in terms of both output and impact. The models explain 5% of the variance in productivity and 23% of the variance in impact, which indicates that while choosing the right advisor may positively influence one’s academic achievements, that decision alone does not tend to make or break one’s research career. The doctoral experience cannot be reduced to the advisor-advisee relationship. Additional factors, such as evolving in an intellectually stimulating environment, affect performance – more in fact than the quality and frequency of one-to-one interactions with advisors. These different degrees of mentorship relationships, such as coordination, cooperation, and collaboration, are not captured in our data. The results also suggest that providing co-authorship opportunities may be a good way for advisors to support their advisees, as it helps increase their research output and impact. Collaboration may be facilitated by students sharing research interests with their advisors, as suggested by the positive relationship between productivity and the similarity of the PhD dissertation and the advisor’s past work. The benefits of working closely with one’s advisor can conflict with the importance of establishing one’s independence as a researcher. Giving students the liberty to diversify their research interests may ultimately provide greater career advantages as they evolve in their careers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".