Bibliographic record
Abstract
In the Exchanges, we present conversations with scholars and practitioners of community engagement, responses to previously published material, and other reflections on various aspects of community-engaged scholarship meant to provoke further dialogue and discussion. We invite our readers to offer in this section their own thoughts and ideas on the meanings and understandings of engaged scholarship, as practiced in local or faraway communities, diverse cultural settings, and various disciplinary contexts. We especially welcome community-based scholars’ views and opinions on their collaboration with university-based partners in particular and on engaged scholarship in general. In this issue, Nazeem Muhajarine talks with Dr. Bryan Smale about his work on the Canadian Index of Wellbeing. Dr. Smale is Professor, Recreation and Leisure Studies, Faculty of Applied Health Science, University of Waterloo, and Director of the Canadian Index of Wellbeing.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".