Proving Intra-Racial Discrimination in the U.S. and Canada: The Room for Making the Artificial Distinction Between Genealogical Relatedness and Race
Bibliographic record
Abstract
This article takes the role of the Devil’s advocate in order to question the judicial willingness to distinguish “race” from comparable notions. It suggests that, depending on the exact circumstances, a defendant can make an arguable case that the alleged intra–racial discrimination is motivated by perceived genealogical relatedness, but not because of belonging to the same “race.” Factually, the defendant claims to believe in being remotely genealogically related to the plaintiff. This is not unworthy of credence, because it is academically recognized that modern genealogy and root tracing can be an imaginative, forged exercise. Legally, this argument is supportable because there are cases holding that “race” or “ancestry” is different from genealogy or “line of descent.” By contrast, such an argument would not work in Canada, because Canada has adopted an expansive interpretation of the impermissible grounds. In particular, Canada includes “ancestry”—despite the fact that it is not explicitly included in their statute—on the grounds of “race”, “ethnicity” and “family status.” This covers more situations that resemble intra–racial discrimination, such as discrimination based on remote or close bloodline (un) relatedness. However, whilst the U.S. courts claim to have adopted a liberal interpretation, they also openly oppose expanding the law and have therefore narrowly interpreted “ancestry” and other impermissible grounds. This makes proof more difficult and leaves open gaps of protection in the U.S.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".