Bibliographic record
Abstract
I take as my text a number of recent court decisions in tort actions, about thirty of them. What characterizes the judgments I examine here is that in them claimants have argued (generally, though not invariably, with success) that something in their culture, their religion or both entitles them to either a finding of liability where liability would not be justified in the absence of that cultural or religious make-up, or, more commonly, greater damages than they would be entitled to in the absence of their specific cultural background.I am not considering claims for loss of culture. In loss-of-culture cases, plaintiffs complain that what they have been deprived of is the language, skills, attitudes, and stories of their ancestors. These plaintiffs have most commonly been First Nations people, but loss-of-culture allegations have not been limited to these groups. (1) Such arguments have been advanced both in the courts (2) and also in the public reparations scheme for government compensation in respect of mistreatment at residential schools. Claimants in those suits maintain that that they do not have a culture or, rather, they lack the intellectual and cultural inheritance they should rightfully have. They may assert that the theft of their cultural legacy from them is a stand-alone cause of action. More plausibly, they aver that their loss of their cultural birthright and its traditional narratives should be counted as a harm, and perhaps even as a distinct head of damages, in the context of some traditional ground of civil liability -- for instance, negligence, battery or breach of fiduciary duty.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".