Bibliographic record
Abstract
While many authors profess that their book would not have been written without the assistance or inspiration of some individual, in our case this is demonstrably true.Had it not been for the leadership demonstrated by Bill Cross in conceiving and executing the Democratic Audit series, neither of the authors of this volume would ever have tackled the project of writing a book about the role of advocacy groups in Canadian politics.We discovered that auditing as large, amorphous, and unresearched a category as advocacy groups was an extraordinarily challenging task.Given these difficulties, we are particularly grateful to Bill, the members of the advisory group that he assembled, and the other authors of volumes in this series for their input and guidance in constructing our study.While we received sage advice from many, the contributions of Richard Sigurdson, Elisabeth Gidengil, and R.K. Carty particularly stand out.As always, our editor Emily Andrew of UBC Press offered excellent guidance at crucial moments.We also benefited tremendously from excellent research assistance from John Roslinski, Angela Gallant, and Jason Roy.Camilla Gurdon and the other members of the editorial team at UBC Press made our manuscript a much better document than it would have been otherwise.Data analysed in the book come from the 2000 Canada Election Study, which was conducted by the
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.271 | 0.199 |
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".