'I Is; Therefore I Am': The Census as Practice of Double Identification
Bibliographic record
Abstract
I examine practices of modern census making with a specific focus on Canadian censuses of population from 1911 – 1951. My analysis builds on the work of two recent and related streams of research in the social sciences. One draws from Foucault's writings on biopower and post-Foucauldian governmentality studies by examining the census as a political technology that produces a specific knowledge or political arithmetic (statistics) of the population so that its forces and strengths can be acted upon by various state authorities. The census is thus understood as a field for the administration of the state. The other focuses on how censuses are socially constructed, on the 'making' of censuses as opposed to the 'taking' of censuses and the use of census data as 'evidence'. These studies document how the interests and political influence of various actors shape census making. The census is thus understood as a particular way of defining, collecting and organising social observations about individuals and not a simple reflection of an empirically existing reality. While the two streams of research have usefully challenged the facticity of census data, they have tended to reinforce a division between the real and the constructed. For if census data is not 'real' but a particular construction then what exactly does it represent? I contend that censuses are part of myriad identification practices that have come to produce subjects who are able to recognise and identify themselves in relation to the categories constructed and circulated by the census. It is through processes of double identification (state-citizen) that census categories come into existence, become facts and can then in turn not only be measured, analysed and assembled (objectification) but also be identified with (subjectification). The presence of such double identification makes an ostensible division between facticity and representation artificial.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".