Bibliographic record
Abstract
This paper emphasizes the importance of cultural competence for tort law by analyzing the Federal Court’s decision in Haj Khalil v. Canada. Given that this symposium in honour of Rose Voyvodic’s life and work is entitled “Re-Imagining Access to Justice,” this paper asks “how do the principles of cultural competence allow us to think about the facts of the Haj Khalil differently. In particular, what would a cause in fact analysis look like if it were informed by the principles of cultural competence?” My analysis proceeds by “reading the silences” or focusing on the unstated assumptions and unexplored elements of Haj Khalil’s story to bring into focus factors relevant to factual causation which remain largely unexplored or undervalued by the Federal Court. 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. While Canadian courts have emphasized the importance of social context for fair judgment, they have not fully come to grips with the implications of social context for judicial decision-making. This is particularly the case within negligence law which remains vexed by the need to maintain an objective standard while simultaneously recognizing the importance of context and circumstance to particular claims.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".