The measurement of racial discrimination: the policy use of statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most ‘multicultural societies’ in the world attempt to act against racial discrimination and some, admittedly less numerous, have adopted pro‐active equality policies based on statistical monitoring. Intensive use of statistics is a requirement for action against indirect discrimination, a legal concept imported into European Union countries since 2000. However, design and collection of statistical data providing information on racial discrimination calls for the establishment of a technical apparatus that raises issues of political strategy and action methodology. Using the results of a comparative study of the statistics used in anti‐discrimination policy in the USA, the UK, Canada, and Australia, this analysis compares the various categories and modes of collection by setting them in context in order to reveal the compromises made between the legal and political imperatives of the struggle against discrimination and the aim of identity recognition within the multicultural project.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it