To Affirm Difference or To Deny Distinction?
Bibliographic record
Abstract
What are the global canons of constitutional equality analysis? Many scholars would say that there are none. National courts cannot seem to agree on whether the guarantee is formal or substantive, intersectional or discrete, open-ended or strictly textual. This Article takes a different tact. There are two budding strands of equality law reasoning: the categorical canons and the difference canons. The former prohibit pernicious distinctions in the law, while the latter affirm individual difference. The difference canons are the more cogent of the two. Categorical equality reasoning leads to underinclusive protection that is discordant with the actual experience of discrimination. Meanwhile, difference equality reasoning quashes budding social inequities before they fester into pernicious “isms.” Categorical courts thus ought to take a page from the difference canons.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.013 | 0.035 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".