Bibliographic record
Abstract
THE 1993 ELECTION is not just an abstract topic of research for me.I was personally involved in the events before, during, and afer the ballots were cast and counted.So when Ken Carty, an authority on Canadian political parties, invited me to write a book about the election, I was delighted to accept.It gave me a chance to revisit an important part of my own life.At Preston Manning's invitation, I went to work for the Reform Party in May 1991.I initially held the title of Director of Policy, Strategy, and Communications, which was later changed to Director of Research to better refect what I was actually doing.From the beginning, I wasn't entirely happy in my job.I supported Reform's agenda, including better representation for Western Canada, Senate reform, balanced budgets, and opposition to one-sided demands from Quebec for constitutional change.However, I had trouble ftting into Reform's nascent organization.Tat I had no real experience in partisan politics made things even more difcult.I lef my paid job with Reform at the end of 1992 and was dismissed as an informal adviser in August 1993.At the time, I blamed Manning for the breakdown in
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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 | 0.001 | 0.005 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.536 | 0.337 |
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".