Book Review of <i>Identity Captured by Law: Membership in Canada’sIndigenous Peoples and Linguistic Minorities</i> by SébastienGrammond
Bibliographic record
Abstract
In Identity Captured by Law, Sébastien Grammond assesses the constitutional and international legality of rules that control membership in Indigenous societies and the official language minorities of Canada. Grammond’s main argument is that Indigenous and minority membership rules do not violate legal commitments to equality if there is sufficient correspondence between the legal criteria that determine membership and the actual criteria that group members themselves deploy to define themselves. Membership rules based on a racial conception of ethnic identity are less likely than those based on cultural or relational conceptions of ethnic identity to correspond to actual identities and therefore are more likely to violate equality rights. This argument requires a substantive as opposed to formal conception of equality, which Grammond develops and defends at some length. Comprehending equality in substantive terms means membership rules are not inherently discriminatory but instead that their constitutional and international legality rests on the extent to which they improve the situation of the group in question as opposed to simply oppressing or stereotyping individual members of the group.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".