Bibliographic record
Abstract
This chapter covers the years between 1926 and 1930 which Ida Greaves spent at McGill University in Canada, as an undergraduate and masters student. Montreal in the 1920s was a booming city, but one with deep-seated economic and social divisions. The Department of Economics and Political Science in which she enrolled was chaired by Stephen Leacock, better-known as a popular humourist writer. But he was also an economist in the institutionalist tradition. Ida Greaves was trained to see economic behaviour as being shaped by evolving institutions. His student, Ida Greaves, was trained; she and her friend Betty Archdale were the only women students in the Department, and both gained first-class honours in their final degree in 1929. Ida Greaves stayed on at McGill to study for a masters degree in research. Completed in 1930, it focussed on the problems in the black Caribbean communities of Quebec and Ontario, and the very limited economic opportunities which were available to them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".