Every issue of the Canadian Journal of Occupational Ther-apy includes one or more articles that really tickle my intellect. It is one of the best parts of this job
Bibliographic record
Abstract
These arti-cles make me step back and reflect by offering ways to enhance my every day work. These articles are ones I go back to and re-read, recommend to colleagues, or include in the reading packs of the courses I teach. Sometimes what attracts me to a particular article is the data collection method(s) and the potential applications in my own research. An example is the Harding et al. (2009) study, which used photographs as one method to understand participation in out-of-school activi-ties among children with disabilities. Other articles stand out because the specific topic or population intersects with my own work or the work of one of my doctoral students. These articles offer new interpretations or alternative explanations we may have not considered. One example is the qualitative study by Turpin, Leech and Hackenberg (2008), which explored childrens ’ experiences of having a parent with multiple scle-rosis. Another example is the Fabio and Chaudhury (2008) systematic review of literature on falls and the physical envi-ronment. Articles also stand out for me because they challenge my thinking about some aspect of our profession or our core
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.248 | 0.050 |
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".