Bibliographic record
Abstract
In the United States, employers, schools, and governments can face two competing legal requirements regarding racial classifications: on the one hand, there are legal restrictions against conscious uses of racial classifications, and on the other hand, there are rules forbidding racially disparate impacts. Growing use of machine learning and other predictive algorithmic tools heightens this tension as employers and other actors use tools that make choices about contrasting definitions of equality and anti-discrimination; design algorithmic practices against explicit or implicit uses of certain personal characteristics associated with historic discrimination; and address inaccuracies and biases in the data and algorithmic practices. Justice Rosalie Abella’s approach to equality issues, highly influential in Canadian law, offers guidance by directing decision makers to (a) acknowledge and accommodate differences in people’s circumstances and identities; (b) resist attributing to personal choice the patterns and practices of society, including different starting points and opportunities; and (c) resist consideration of race or other group identities as justification when used to harm historically disadvantaged groups, but permit such consideration when intended to remedy historic exclusions or economic disadvantages.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| 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".