Bibliographic record
Abstract
To the Editor: Innovation is gaining attention alongside research and education as legitimate scholarly activity. In our experience, identifying an individual as an informal and unofficial “Innovation Forager” was crucial to stimulating academic innovation, enabling success in developing our hospital’s academic strategy and micro-grant project.1 With a supportive manner, genuine interest, enthusiasm, and academic curiosity, it was often the Innovation Forager who first identified for others that their work was innovative and worthy of turning into scholarly activity. We found that many people did not self-identify their work as innovative or academic in nature. They therefore had not sought out or responded to traditional academic grant strategies or requests for proposals. In retrospect, most of our innovation projects were prompted by someone culturally connected with the clinical environment and constantly on the lookout for innovation potential. Without the encouragement of the Innovation Forager, most of our scholarly low-hanging fruit would have spoiled on the vine. The role of an Innovation Forger may be particularly helpful in peripheral “distributed” academic environments, such as community hospitals, where a culture of scholarship is less prioritized or top of mind. The greatest unrealized academic potential of these distributed sites may be the opportunities for innovation in generalizable, real-world practice settings. It has been said that innovation often occurs in the fringes, unencumbered by bureaucracy and red tape. Capturing the scholarly opportunity of a community site may require contextualizing and customizing the methodologic design and approach to academic development, as in our discovery of the role of an Innovation Forager. It is our belief that an Innovation Forager, someone to stimulate and support those who would not initially think their ideas and work are of academic merit, may be an important ingredient to encourage innovation scholarship, and thereby furthering academic knowledge. Formally identifying, designating, and researching the role of an Innovation Forager as part of academic development may be worthwhile. Thomas Ungar, MD, MEd, FRCPC Chief of psychiatry, North York General Hospital, and associate professor of psychiatry, University of Toronto, Toronto, Ontario, Canada. Madalyn Marcus, PhD Clinical psychologist (supervised practice), WaterStone Clinic, Toronto, Toronto, Ontario, Canada; [email protected]
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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".