Tolerance of Homosexuality in 88 Countries: Education, Political Freedom, and Liberalism
Bibliographic record
Abstract
Researchers have repeatedly found a positive correlation between education and tolerance. However, they may be victims of an unrepresentative sample containing only rich Western liberal democracies, where political agendas have a liberalizing effect on curricula. In this paper, we specify the relationship between education and liberal attitudes by analyzing data on educational attainment and tolerance of homosexuality (one dimension of liberalism) drawn from a heterogeneous sample of 88 countries over the period 1981–2014. We argue that nonliberal political agendas in some countries undermine the supposed universality of the positive relationship between educational attainment and tolerance of homosexuality. In relatively free countries, education is indeed associated with greater tolerance. However, in relatively unfree countries, education has no effect on tolerance and in some cases encourages intolerance. Specifically, our analysis demonstrates that education is associated with tolerance of homosexuality only when regimes energetically promote liberal‐democratic values. The larger theoretical point is that the agendas of political regimes shape civic values partly via education systems. Especially in an era when democracy is at risk in many countries, it is important to recognize that education is not always a benign force.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".