Bibliographic record
Abstract
To the Editor: Innovation is increasingly being recognized as a legitimate scholarly academic activity, alongside research and education.1,2 However, this emerging emphasis placed on innovation in academic medicine is not reflected within the process of measuring and documenting scholarship for academic promotion. In fact, innovation scholarship activity is often a challenge for promotions committees in academic medicine. This activity is usually listed by promotions applicants as educational scholarship or “creative professional activity.”3 As a senior promotions committee member, Dr. Ungar often witnesses the struggle when evaluating the impact of innovation. This is further compounded because innovation scholarship activity is sometimes viewed with skepticism and mistrust in contrast to research. One well-accepted title or role that academics use to demonstrate their activities is principal investigator (abbreviated “PI”) for academic research. We posit that the creation of an additional PI title, that of “principal innovator,” would more accurately capture the abundance of work that is being conducted within academic medical innovation. Principal innovator is already a job title in the information technology sector where innovation has long been a focus of merit. Recognition of the role of a principal innovator and accommodation for documentation of innovation activity would help academic institutions better understand, conceptualize, and be able to evaluate the quality, impact, and nature of innovation-based academic activity and scholarship. Defining a principal innovator as the other PI would go a long way toward legitimizing and valuing academic innovation. Thomas Ungar, MD, MEd, FRCPCChief of psychiatry, Mental Health Department, North York General Hospital, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; [email protected] Madalyn Marcus, PhD, CPsychClinical psychologist, Southlake Regional Health Centre, Newmarket, Ontario, Canada.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.060 | 0.036 |
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".