The problems of false comparions: Animal discourse and the anti-choice movement.
Bibliographic record
Abstract
Advocacy for the protection of animal welfare and women’s right to reproductive choice have little, if anything, in common. It is productive, then, to question the recurring association of these unrelated ethical issues. If one feels compassion for the plight of the nonhuman animal, the argument goes, then it is morally inconsistent to neglect the fetus. An understanding of the incongruous political contexts at play between abortion and animal welfare effectively repudiates this argument, but a more important question must be answered: why is this strange argument so pervasive? In brief, it is the uniform legal marginalization of women and animals that animates this false comparison, which is instructively analyzed through the theoretical lens of ecofeminism. The leading Canadian judgments of R v Morgentaler and R v Ménard exemplify the socio-legal contours of ‘otherness’ outside the locus of patriarchal dominance. This renders a broadly transferable framework of oppression at the hands of the law; examining jurisprudential examples of displacement of agency, fragmentation of the self, and instrumental objectification, in both contexts, provides a useful starting point in a consideration of the broad intersections between the legal treatment of women and animals.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.103 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.020 | 0.014 |
| 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".