Bibliographic record
Abstract
My tenure as the founding director of the Saskatchewan Population Health and Evaluation Research Unit (spheru) coincided with a personal transition and a professional opportunity.My personal transition was acceptance of a full-time position in a university setting after two and a half decades of work in government and international consulting.At the same time, I was presented with a professional opportunity to enter into a partnership with the Community-University Institute for Social Research (cuisr).Embracing the idea of a partnership with cuisr when I took on the spheru directorship seemed only natural.cuisr and spheru, in the early years, were similar to conjoined twins.Several of spheru's researchers were also affiliated with cuisr; community-based research was one of our several population health research interests and one shared with other cuisr researchers.Building a partnership based on this foundation was a logical step in the evolution of both organizations.Jim and Kate honestly comment above on some of the tensions that can beset the community-university relationship.They also note that "we" (the academy) are also "they" (the community), with the blurry line between the two that comes into focus only when their differing knowledge premises, subject positions, and potential roles in creating healthier communities are
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.501 | 0.414 |
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".