Counting Things and People: The Practices and Politics of Counting
Bibliographic record
Abstract
Many scientific and nonscientific activities involve practices of counting. Counting is, perhaps, the most elementary of numerical practices: an ability to count is presupposed in arithmetic and other branches of mathematics, and counting also is part of innumerable everyday and specialized activities. Though it is a simple practice when considered abstractly, in specific cases counting can be quite complicated, contentious, and socially consequential. Categorical judgments determine what counts as an eligible case, instance, or datum, and these judgments can be difficult and controversial. By focusing on such difficulties, this article aims to elucidate practices that are crucial for the production and stabilization of natural and social orders. Cases discussed in the article are provisionally divided between counting (nonhuman) things and counting people. Cases of counting things include scientific practices of counting the number of human chromosomes and forensic procedures for counting matches in DNA profiles. Cases of counting people include estimates of crowd size and counts and recounts of election ballots. Counting people not only is a matter of including an object or person in a class or group, but also involves reciprocal performances in which the counted objects are complicit in, or resistive to, the social production of counts. Variable, and otherwise troubled and contested, instances of counting are used to elucidate the numeropolitics of counting: how assigning numbers to things is embedded in disciplined fields, systems of registration and surveillance, technological checks and verifications, and fragile networks of trust.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.165 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".