Domestic Misfits, Social Physics and the Problem of International Statistical Standardization
Bibliographic record
Abstract
ABSTRACT During the 19th century, driven by the ideas of Adolphe Quetelet, population statistics were actively being developed and debated by many nascent nation‐states. The International Statistical Institute (ISI) was one of the premier statistical organizations of the 19th and 20th centuries that possessed a very international and prestigious membership including representatives from across Europe, Latin America and Japan. During this “first principles” period, statisticians were openly grappling with both the practical questions related to the “conditions of production” of data as well as questions of statistical theory and good practice. This article analyzes some of the debate the ISI held on the problem of international social statistical category standardization that the ISI encountered as well as the factors and “tests” they used to resolve these issues.
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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 | 0.096 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".