Bibliographic record
Abstract
In this paper, I argue that properly analyzed, the tort of negligence offers a more effective means of addressing racial injury than does a specific tort of intentional discrimination. The most common racist acts are subtle and cause indirect harm, characteristics that intentional torts are not designed to address. Furthermore, because causing harm through negligence is less stigmatizing than intentionally inflicting harm, courts are more likely to acknowledge the actual injury and provide for redress if the action is framed in negligence. Finally, establishing a tort of negligent racial discrimination would be well within the recognized jurisdiction of common law courts to develop the common law on an incremental basis and would be consistent with the Supreme Court of Canada’s decision in Bhadauria. After outlining the advantages of using a negligence framework to address racial injury, I set out the elements of a potential tort of negligent discrimination, highlight the challenges of developing and applying a tort of this nature and suggest how these challenges may be overcome. Ultimately, I conclude that even though intentional torts may appropriately deal with racist taunts and overtly racist acts, the widespread racism in North American culture, including systemic racism, is addressed more effectively through the negligence framework.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".