Bibliographic record
Abstract
◇ ◇ ◇ ◇ ◇ had before him an urgent request from Britain's colonial secretary, Joseph Chamberlain.To consolidate wartime gains and secure British control in South Africa, Chamberlain was keen to recruit female teachers 1 from Canada, Australia, and New Zealand to assist Britain in educating Boer 2 children living in Transvaal and Orange River Colony concentration camps.3 He asked for forty of Canada's top teachers and provided Minto with details of the scheme.In doing so, he strongly encouraged Canadian officials to undertake personal interviews with potential candidates, declared that Roman Catholics should not be considered for these positions, and demanded that "no teacher is selected who is opposed to British rule in South Africa." 4 Federal officials went straight to work recruiting Canada's best and brightest young female teachers.Working closely with provincial authorities, they issued a call for volunteers and were overwhelmed by the response, for in just a few months over five hundred women put forward their names for consideration.5 Humanitarian and imperial rationales intertwined as the scheme was developed.Recalling her briefings en route to the camps, Isabel Perry, a teacher from Montreal, noted that "our mission was a very important one, equally as important as that of the soldiers, for it was to implant feelings of loyalty to the British flag in the hearts of the Dutch children and to endeavour to reconcile Dutch women to Cape
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.287 | 0.094 |
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".