Preface and Acknowledgments
Bibliographic record
Abstract
I frst set foot in the Maritime Peninsula on Labour Day weekend in 1999.It was an accidental arrival, of sorts.The spring before, after being accepted into several undergraduate programs for engineering, I decided that I was more likely to succeed in the liberal arts.Too late to apply anew to schools in Ontario, where I grew up, I applied to several universities out of province and ended up at Acadia University in Wolfville, Nova Scotia.Though not entirely random, I knew next to nothing about Mi'kma'ki, Acadia, or Nova Scotia before the plane touched down on that Labour Day weekend.Despite my ignorance, the decision to attend Acadia changed my life and, in many ways, bears responsibility for the book before you.It was at Acadia where I met three of the four people whose infuence and support has most deeply shaped this book: First, this is where I met my wife, Susan, whose encouragement, support, and counsel helped me carry on when it looked like this project might not come together.Susan, my two children -both born while working on this project -and my parents, in-laws, and siblings have been important supports throughout this process; I should also note all of their abundant patience!"One more minute" is a common refrain of mine, especially when writing (among other things).It is a known nuisance to all.Another infuential person I met at Acadia is John Reid, who, in my fourth year, came to our campus to recruit for Saint Mary's University's Masters Program in History.Little could I have known at the time just how deeply John's scholarship would come to shape my own.I am grateful that John has continued to be a mentor over the near two decades since I completed my Master's degree with him.Several other people in x
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.307 | 0.211 |
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".