So Many Clever, Industrious and Frugal Aliens”: Peter Sandiford, Intelligence Testing, and Anti-Asian Sentiment in Vancouver Schools between 1920 and 1939
Bibliographic record
Abstract
In this article I explore a racist period in the history of Canadian education that has received scattered attention from scholars of Canadian educational history. This particular example of racism involved the use of intelligence tests to confirm notions of racial superiority and inferiority. For the 1925 Putman-Weir Survey of the School System in British Columbia Professor Peter Sandiford, of the University of Toronto, subjected the province’s schoolchildren to a regime of intelligence tests. This became problematic when he found that the results achieved by “Oriental” or Asian (Chinese and Japanese Canadian) schoolchildren were superior to those achieved by white children. I discuss how this instance of anti-Asian sentiment fits into the broader educational history of Vancouver’s schools and the troubling pattern of racism within the overall history of British Columbia. This use of scientific racism through intelligence testing is disturbing and warrants closer examination within the context of its own time period. It is a cautionary historical tale about popular social attitudes and the drive for a dominant Anglo-white racial identity in the province.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.051 | 0.022 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".