Postdoctoral scientists are mentors, and it is time to recognize their work
Bibliographic record
Abstract
Academia often fails to recognize the important work that supports its functioning, such as mentoring and teaching performed by postdoctoral researchers.This is a particular problem for early-career researchers, but opportunities exist to improve the status quo.One adjective commonly applied to postdoctoral scientists (postdocs) and their work is "invisible": invisible scholars [1], invisible innovators, invisible mentors.These metaphors describe the reality of the labor performed by many postdocs; these tasks are often expected of them by the demands of the academic job market but seldom formally credited to them.Owing to their (often) dual role as employees and trainees, they are expected to fulfill the obligations of both while reaping the benefits of neither.For example, postdocs are not always eligible to apply for independent research funding and, therefore, need to split credit with researchers with more job security.Moreover, when postdocs contribute to teaching, they are not always listed as instructors of record (designated as in charge of the course), and, therefore, this work may not be consistently recognized in job applications.They are also no longer eligible for training, grants, or fellowships exclusive for students, and in many institutions, postdocs do not have health benefits coverage, unlike graduate students and faculty, for whom coverage is often mandatory.For example, in the province of Que ´bec, Canada, health insurance coverage for postdocs can vary according to immigration status, history of foreign residency, medical history, immigration status of spouse or common-law partner, intercountry agreements, specific job title, and university partnerships with private insurance companies.For postdocs wanting to pursue an academic career, this lack of recognition can put them at a serious disadvantage, as uncredited work can come at the expense of contributing to research projects.This conundrum is particularly true when it comes to mentoring graduate students.Postdocs are highly trained, up-to-date on the literature, have a fresh eye on the state of the art in their field, and are often leading experts in emerging methodologies and approaches.In fact, data suggestAU : PleasenotethatasperPLOSstyle; }data}takespluralverb:Hence; }Infact; datasuggeststha that over a 5-year period, postdocs in the life sciences outpublish graduate students and faculty [2], which demonstrates their familiarity with cutting-edge scientific research subjects and practices.Being early in their career, they have likely very recently experienced
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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 | 0.078 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.023 |
| Insufficient payload (model declined to judge) | 0.033 | 0.027 |
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".