Bibliographic record
Abstract
The criminal law has at least two goals: to provide a degree of protection to a variety of individual and collective interests, and to communicate to those to whom it applies that those interests are protected. The question I consider is whether the criminal law should be used to advance the second goal independently of its use in advancing the first. Drawing on what I refer to as non-comparative egalitarianism, I argue that it should not. After developing a general argument for this claim, I turn to considering its implications for the criminalization of hate speech, focusing specifically on a line of argument found both in the Supreme Court of Canada’s s.2 jurisprudence as well as Jeremy Waldron’s recent book,The Harm in Hate Speech. I also briefly consider a structurally similar, but broader argument – recently defended by Alon Harel – which suggests that there is a constitutional duty to criminalize conduct that would, if engaged in, interfere with a person’s dominion over how her life goes, regardless of whether criminalization would or would not drive down the actual incidence of the targeted conduct. I claim that egalitarians should not recognize any such 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".