Bibliographic record
Abstract
There are many people whose encouragement and support made this book possible.Perhaps it is best to start in the fall of 1996 when it all began.At that time I met Dr. Barry Gough, who became my thesis advisor and mentor at Wilfrid Laurier University.While this book has changed considerably from the original thesis, Barry's careful guidance and advice was instrumental in my development as a historian.Dr. David Monod and Terry Copp, also professors at Laurier, were an equally important part of this process, and for that they have my eternal thanks.The book truly took form while I was writing a series of narratives on the politics of naval expansion for the Second World War Official History Naval Team at the Directorate of History and Heritage.Michael Whitby, my boss and friend, deserves the lion's share of the credit for my converting these narratives into a book.Mike was also an incredible sounding board for ideas, usually worked out at the local pub, as well as a source of constant
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.258 | 0.215 |
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".