Bibliographic record
Abstract
I remember looking out the airplane window excited to catch my first glimpse of Siberia as I arrived in Omsk.Exploring the city, I found that I had travelled halfway around the world to wind up in the Russian version of a place I had spent my adolescence dreaming of escaping.Somehow, I had managed to substitute the gateway to the Canadian Prairies -Winnipeg, Manitoba -for another confluence of murky rivers in its Eastern counterpart.Learning to embrace the familiar landscape and climate, including the winters, taught me to appreciate the place I thought I had left behind.Many people and institutions facilitated this journey from Winnipeg to Omsk and back again.At the University of Manitoba, the late Margaret Ogrodnick started me on this path through her kind encouragement.At Carleton University, Jeff Sahadeo, Piotr Dutkiewicz, and the late Carter Elwood contributed to my intellectual development.From the start of my arrival at the University of Alberta I've benefited immensely from Heather Coleman's commitment to her students; she truly taught me how to be a historian.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.152 |
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".