Bibliographic record
Abstract
The invitation from William Cross to join a team of fellow political scientists in an audit of Canadian democracy was too good to turn down.I am grateful to Bill for having asked me to become a part of this project and for having guided all of us through several collective meetings and various iterations of our respective tasks.Like students with an essay deadline looming, we were reminded with great tact (and a quiet forcefulness that Bill has mastered) that we had commitments to meet.It was a superb group to work with.I am thankful for our regular meetings over a two-year period, the helpful assessments we offered one another, and the friendships that were formed.Political science in Canada is a strong and proud profession which, as the Democratic Audit project bears witness, has a great deal to offer students, policy makers, and the general public.Bill Cross and the Canadian Studies Centre at Mount Allison University deserve full credit for having undertaken this initiative, the first of its kind in Canada.Bill Cross, as it turned out, was only one-half of the team issuing directives, suggestions, and advice.The other half was Emily Andrew who, as senior editor at UBC Press, kept us fully informed about her views on the content of our individual undertakings and about the Press's standards and requirements.All who contributed to this series found Emily an absolute gem to work with.We are thankful for her contribution.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.091 |
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".