Bibliographic record
Abstract
Once upon a time I moved to a big city and found myself broke and unemployed.When I was plucked from an unemployment seminar to be a receptionist for a talent agency, I merrily embraced it.I was grateful for the job and resigned to a life beyond the hallowed halls of the university.But through a strange twist of fate I soon found myself an agent at that company selling Canadian talent to Canadian organizations.As a former student of life writing, I realized very quickly that people liked a good story.A story of origins.A story of struggle and discovery.Life stories created an illusion of access to a personality whose accomplishments were already self-evident.Life stories could clinch a deal.And so there, in a cubicle in Toronto, began the first glimmerings of this book and my return to academia.Between then and now, I have been helped and inspired along the way by countless people.And while it is customary to thank our loved ones last in the Acknowledgements (why should that be?),I must begin there, because when you have been lucky enough to find a partner who shares your burdens, prods you on, listens carefully and advises, keeps you fed and sane, and knows when it is time for a hug and when it's time for a glass of wine, then you too recognize that your successes are built on their love and labour.Thank you, Ryan Veenstra.For everything.I have also been blessed with an extraordinarily supportive community of scholars who share my interest and fascination with Canadian popular cultures.For over ten years now Lorraine York has been my mentor, friend, and colleague, and while I cannot ever hope to repay her generosity and many kindnesses, I can, at least, try to pay it forward.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.533 | 0.450 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".