Gender Differences Towards Gender Equality: Attitudes and Perceptions Of College Students
Bibliographic record
Abstract
In this article we describe a research project where the objective was to analyze attitudes toward gender equality among male and female university students in Quito, Ecuador. The study methods included both quantitative and qualitative phases, and the results were then integrated. In the first stage of the research, we used inferential statistics to analyze differences in attitudes towards gender equality among 75 men and 75 women. In a next phase, we used constructivist qualitative methods to analyze the narratives of 22 subjects participating in three focus groups. In the quantitative phase, we found a statistically significant difference when comparing attitudes toward gender equality between the two groups of participants, where women were more favorable toward gender equality. In the qualitative phase we identified a common narrative theme in which women felt that they were in an unfavorable situation in terms of gender relations. In addition, a category emerged that described an awareness and a desire on the part of women to change this situation. Finally, we discuss the findings from previous research as well as theoretical aspects of gender equality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".