Bibliographic record
Abstract
Tort law can only deliver justice if decision-makers exercise cultural competence; one cannot see the true suffering of the other by looking through a uni-cultural lens. A uni-cultural lens blurs the differences between people’s lived experiences and obscures the decision-maker’s capacity to understand the suffering of others, thereby silencing that suffering. This silencing in turn undermines the aims of tort law. This paper emphasizes the importance of cultural competence for tort law by analyzing the Federal Court’s 2007 decision in Haj Khalil v. Canada. The Federal Court held that immigration officials did not owe a duty of care to Haj Khalil and could not be held accountable for the unreasonable delay in processing her application for permanent residency. It also ruled that the delay could not have caused her losses. I conclude that an examination of the facts that framed Haj Khalil`s claim against immigration officials through a culturally competent lens would open the possibility of a different understanding of causation as it arises on the facts of the case.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".