Bibliographic record
Abstract
We thank Dr. Chow and colleagues for taking interest in our Innovation Report and having ambition to extend our coached peer review technique beyond medical students. Indeed, it is important that we form communities of practice1,2 around early career faculty members looking to up their academic scholarship game. As Dr. Chow and colleagues point out, early career academic clinicians are often asked to perform academic duties beyond their training. Some opt to pursue graduate studies early in their careers,3 but there is no guarantee that these programs will teach them the academic skills they need to thrive. As grants become scarce and publication pressures mount, it is increasingly hard for early career academics to be successful. We agree that opening peer review to become the formative, coaching process we describe may be part of the solution. Similarly, innovations, like group peer review processes,4 can also help those acquiring skills in reviewing articles, providing them with mentors who offer insights into the mysteries of peer review. Another way for early career academics to gain skills is to become apprentices or partake in high-level faculty development communities. Programs like the Academic Life in Emergency Medicine faculty incubator program,5 Harvard Macy’s educators course,6 or the CanadiEM digital scholars fellowship7 are other possible ways for individuals to acquire and develop their skills. Lastly, perhaps institutions should completely reexamine their promotions processes. Not every clinician–teacher needs to be a great researcher. The time and energy it takes to engage uninterested early career faculty in the art of academic scholarship may be better spent changing the hearts and minds (and policies) of our institutions to acknowledge the modern medical school’s diverse forms of scholarship. Advocacy, community service, leadership, and high-quality teaching are some of the ways great clinician–teachers contribute. Perhaps instead of asking everyone to play by the same rules, we could instead acknowledge that our talents vary, and through structural reform celebrate, invest, and reward this diversity accordingly.3 Teresa M. Chan, HBSc, BEd, MD, FRCPC, MHPEAssociate professor, Department of Medicine, Division of Emergency Medicine, and assistant dean, Program for Faculty Development, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; [email protected]; ORCID: http://orcid.org/0000-0001-6104-462X. Eve Purdy, BHSc, MD, MScResident physician, Royal College of Physicians and Surgeons of Canada, Emergency Medicine, Queen’s University, Kingston, Ontario, Canada.Brent Thoma, MD, MA, MSc, FRCPCAssociate professor, Department of Emergency Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; ORCID: http://orcid.org/0000-0003-1124-5786.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.046 | 0.058 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".