The Potential for Violence Against Homosexuals and Strategies of Advancing Tolerant Gender Social Norms and Democracy
Bibliographic record
Abstract
Abstract In this chapter, we first present an estimate of the potential for homophobic violence driven by religiously motivated extremism, estimated from data from the World Values Survey 2017 to 2022. on a population-weighted basis, 52.5% of the world's population can be classified as homophobic, that is, they disapprove of having a homosexual neighbour, and 12.8% of the population not only disapprove of having a homosexual neighbour, but also strongly believe that it is an essential part of democracy for religious institutions to interpret the laws 1. 2% of the world's population now not only are homophobic and believe that it is an essential part of democracy for religious institutions to interpret the laws, but also strongly believe that political violence is justified. We then present country estimates of this extremist religiously motivated homophobic population with the Philippines, Malaysia, Kenya, Ecuador, Zimbabwe, Canada, Spain, Nicaragua, Mexico, and Iraq leading the way. We then present a parametric factor analytical derived index of tolerance, social gender norms, and democracy, and show the results for the countries as a whole, and for their Muslim and Orthodox populations. we also analyze in this chapter homonegativity in the wider social context and discuss the very close relationship between homonegativity and phenomena, such as religious particularism, and restrictive gender, norms, documented by the United Nations Development Program and find evidence of the strong relationship between homonegativity and anti-Semitism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".