Towards Gender Harmony Dataset: Gender Beliefs and Gender Stereotypes in 62 Countries
Bibliographic record
Abstract
The Towards Gender Harmony (TGH) project began in September 2018 with over 160 scholars who formed an international consortium to collect data from 62 countries across six continents. Our overarching goal was to analyze contemporary perceptions of masculinity and femininity using quantitative and qualitative methods, marking a groundbreaking effort in social science research. The data collection took place between January 2018 and February 2020, and involved undergraduate students who completed a series of randomized scales and the data was collected through the SurveyMonkey or Qualtrics platforms, with paper surveys being used in rare cases. All the measures used in the project were translated into 22 languages. The dataset contains 33,313 observations and 286 variables, including contemporary measures of gendered self-views, attitudes, and stereotypes, as well as relevant demographic data. The TGH dataset, linked with accessible country-level data, provides valuable insights into the dynamics of gender relations worldwide, allowing for multilevel analyses and examination of how gendered self-views and attitudes are linked to behavioral intentions and demographic variables.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".