Problematic Modernization: The Extent and Formation of Muslim Antipathy to Homosexuality
Bibliographic record
Abstract
Perhaps most people assume that Islam must be opposed to sexual diversity and gender equality because their first thoughts are of fundamentalist Islam based on rigid interpretations of the Quran. Muslims are not immune to this assumption either, with much of the evidence discussed below indicating a common sense understanding of Islamic prohibition of homosexuality. 1 There is, however, a wider public culture in the West in which we associate all of our present mainstream religious traditions with antipathy to homosexuality. The first wave of gay liberation analyses focused keenly on Christianity’s contribution to ideologies of homosexual oppression (Altman, 1993 [1971]) and most accounts of the progress of LGBTIQ rights include the gradual secularization of Western societies as a key explanatory factor (Weeks, 2007). To this day, religiosity seems to be a key explanatory variable in accounting for homophobia amongst populations and, moreover, antigay prejudice often appears as the most extreme form of discrimination in religious populations (Leak and Finken, 2011). In many Western countries, conflicts between religious and queer rights groups have existed since the early days of gay liberation and continue in the present era of increasing queer rights. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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".