Bibliographic record
Abstract
I would like to acknowledge the Lions Club for their support, which helped to make this research possible. To my wonderful colleagues at the Federation of Canadian Municipalities: It was an immeasurable pleasure to work with such a delightful and dedicated group people, who aspire to make a difference in Canada’s communities. Special thanks to John Purkis and Doug Pollard for their guidance, understanding, and support throughout this journey, and to Sue Welke and Silvestre Fink for their insight on the QOLRS program. I would also like to acknowledge “the crazy folks in Corporate ” who made me feel at home outside my department, and kept my belly full of laughter (and tasty snacks). Thank-you to my academic advisors: Åake Thidell, Don Huisingh, and Vladimir Dobes, whose patience and support spanned the globe (quite literally), and for their continued faith and encouragement that it would all come together, eventually. You are all wonderful! I would like to express my sincere appreciation to the knowledgeable veterans, who so
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.325 | 0.190 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".