Who Counts Now? Re-making the Canadian Citizen
Bibliographic record
Abstract
This paper considers the implications of the 2010 cancellation of the Canada mandatory long-form census in terms of citizenship and the citizen-state relation. Inspecting census questions, Statistics Canada publications, and the arguments of ethnocultural groups pushing for reinstatement of the census, we find a version of citizenship rooted in ethnocultural group membership and the mosaic metaphor. The second part of this paper seeks an historical explanation for the cultural shift away from this version of citizenship that allowed for the cancellation of the census. Here we discuss the state monopolization of gambling. Inspecting advertising and government policy we find a rhetoric of counting that encourages a risk-assessing, individualized, neoliberal, and utilitarian citizen.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.038 | 0.036 |
| Scholarly communication | 0.031 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".