L’assimilation et l’application des connaissances
Bibliographic record
Abstract
ful consideration. It suggests that philosophical fit overrides issues of evidence, that we first vet the evidence for fit with our basic ideas, and only if there is a fit do we consider the evidence for its possible uptake and translation into practice. If this is indeed the case, it suggests that our approach to supporting knowledge uptake or translation needs to be reconsidered; it further suggests that our approach to knowledge generation needs to be reconsidered—the latter is, prima facie, untenable. A profession based in science needs to be open to evidence—to be sure, it needs to be critical in its consideration of the evidence, applying the highest quality standards, but it needs to be open to evidence! With her opening words, Dr. Case-Smith offered an important insight into the ethos of our profession. If she was correct, and the evidence, at least with respect to the place ABA holds in the occupational therapy literature, suggests that she was, her words require us, as a profession, to elucidate our philosophies and examine them carefully from the perspective of science. Dr. Case-Smith’s insight also holds an imperative for us as individual, autonomous practitioners. Notwithstanding the role of the profession in knowledge uptake and translation, each of us is, ultimately, the gatekeeper for our own knowledge uptake and translation into practice. Accordingly, each of us needs to be self-reflective; each of us needs to examine the factors we bring to bear in considering the emerging evidence so we can best enable our clients’ occupation!
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 | 0.120 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.036 | 0.030 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".