Bibliographic record
Abstract
This book would not have been possible without the help and support of the individuals who made up the campaign team for the New Democratic Party in the election campaign of 2005-06.These dedicated and decent people often went out of their way to leave me with insights that would otherwise have been impossible to divine.I thank you all for your generosity.However, I would like to single out Brian Topp for special acknowledgment.As campaign co-chair in the 2005-06 contest, Brian made it possible for me to obtain access to internal war room conversations that would normally have been out of bounds to a researcher.He was generous with his explanations about how the war room worked.He never ducked tough questions or tried to influence what I was writing.Even during his bid for the ndp leadership in 2011-12, when every waking minute of his day was taken up with winning that contest, he made himself available to me.For a chief strategist of a major Canadian political party to be this open is truly remarkable and comes, I believe, from his genuine desire that average people understand why politicians and political parties do what they do on the campaign trail and why the votes of everyday
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.360 | 0.262 |
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".