Bibliographic record
Abstract
While the census is sometimes understood to be an objectifying practice that constructs and makes up a population, in this paper I am concerned with how it is necessary to produce census subjects in order to construct population. By drawing on formulations by Latour, Deleuze and Law, I conceive of census taking as a practice performed by heterogeneous socio-technical arrangements of actors – humans, paper forms, categories, concepts, definitions, topography, geography – whose mediations, interactions and encounters produce census subjects. It is through the relays and interactions between varying and never fixed technological, natural and cultural actors that census taking is performed. I analyse these arrangements as constituting agencements, which focuses our attention on how agency and action are configured by and contingent upon the socio-technical arrangements that make them up. Agencements assume different socio-technical configurations and thus construct different social realities and populations that cannot be captured in a single account. The argument is advanced through an account of the taking of what was declared the first ‘scientific’ enumeration of ‘Indians’ and ‘Eskimos,’ the Aboriginal inhabitants of the Canadian Far North in 1911. I argue that the agencements were not able to bring forth the subjectivities necessary to construct population in the Far North. Not able to find subjects then, census taking could not produce nor construct a population in the Far North and the practice of census taking ended up creating a record of a census ‘other’ – an indeterminate multitude that could not identify and could not be identified as part of the population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".