Making Up Soldiers: The Role of Statistical Oversight and Reactive Path Dependence in the Effectiveness of Canada’s WWII Mobilization Program 1940-1943
Bibliographic record
Abstract
This paper demonstrates the relationship between specific surveillance technologies and state actors’ capacity to assert governmentally ascribed social categories, and their associated performances, onto individuals and populations. During the Second World War in Canada, the government passed legislation to conscript individuals into the Armed Forces. The program commenced in 1940, mandating a registration of all those over the age of sixteen. The conscription of men classified to be “mobilized” began soon after, however, the effectiveness of the National Registration and mobilization system to conscript these identified individuals increased dramatically with the adoption of a new set of surveillance technologies in March 1942. These changes led to significant increases in the number of men that were conscripted per month after 1942, pointing to the capacity of these new policies and technologies to assert greater statistical oversight over system staff and its targeted populations. These surveillant practices and technologies also prompted the development of a form of reactive path dependence. Together, these two points served as key factors which worked to alter the observable performances of individuals classified for conscription.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".