Bibliographic record
Abstract
Objectively, the low point of this whole process occurred on an otherwise pleasant day in June 1998.While I was returning to Vancouver from a research trip to Oregon and Washington states, my devoted '88 Chevy Sprint was totalled just south of Bellingham when it was involved in a multiple rear-ender initiated (ironically enough given the topic of this book) by an inattentive cross-border shopper from North Vancouver.Persons less paranoid than me would likely dismiss a Canadian tourist in the United States barrelling his automobile into the back of a car Wlled with research notes on the development of tourism and consumerism in North America as pure coincidence.I know better.This event -coupled with a National Archives employee's attempt to charge me an international postage rate on a shipment of photocopies from Ottawa to Vancouver a year earlier -alerted me early on to the possibility that this project would not be completed quickly or easily.Finishing a project such as this entails persevering through what seems like an endless series of Pyhrric epiphanies.Many people have helped me to negotiate the arduous route to completing this book.I came to Queen's to do my MA in 1993 in part because a very wise professor at UBC told me that I could "do a lot worse than work with Ian McKay."I told Ian this the Wrst day I met him.He laughed.I considered that a good sign.It was.I stayed at Queen's for my PhD for two reasons.One was Ian.The other was Karen Dubinsky.Karen has provided me with innumerable insights, sage advice, and constant encouragement over the past decade.Many thanks as well to James Carson, John Holmes, and, especially, Keith Walden for their insights and ideas. My thanks to Alisa Apostle
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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 | 0.005 | 0.016 |
| 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 | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.462 | 0.311 |
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".