Bibliographic record
Abstract
Recently, blogger Anila Muhammad posted “Should Muslims Reconsider AnimalSlaughter on Eid?” on the Canadian edition of the Huffington Post.1 Sheclaims that some animal advocates are asking this question. Of course it is anactivist’s right to raise such an issue, even though it could be offensive to practicingMuslims. In reality, however, the majority of Muslims neither know ofsuch a proposal, nor would they consider its possibility. Boldly claiming thatsome Muslims are calling “for an end to animal sacrifice,” she cites these “notableanimal advocates” and, full of passion and confidence, states that “manyMuslims do not see the tradition of sacrifice to be serving ‘their understandingof Islam.’” Intriguingly, she cites several Qur’anic verses and presents herown understanding of them – an understanding that happens to contrastsharply with the widely accepted narrative of Muslim scholars who base themselveson the Prophet’s actual practice and understanding.Although she presents the arguments from several perspectives (viz., intellectual,religious, social, and economic), I suggest that instead of “pretending”to know the Qur’an and Islamic worldview, she should have stuck withher activist perspective and thus avoided a response from Islamic intellectuals.But the way these activists keep citing the Qur’an to legitimize their argumentsand claiming to know better what Muslims should do not only suggests littlefamiliarity with Qur’anic content, but also exposes them to a rigorous and faircriticism from real scholars of the Qur’an and Islam ...
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".