Attitudes towards Gender Equality of Government Institution´s Workers in Southeastern Mexico
Bibliographic record
Abstract
The objective of this research was to analyze if there are differences between the Institutions of Social and Public Security Assistance and between men and women who work for the government of the State of Campeche, southeastern Mexico. This article uses a quantitative methodology; For this, the Questionnaire "Attitudes towards Gender Equality" (CAIG) was applied, which was prepared by Amelia Sola, Isabel Martínez Bellonch and José Luis Meliá (2003), validated in a Mexican sample by Olga Marfil Herrera (2006) with an alpha of Cronbach's .885. The sample was composed of 212 people, 79 women and 133 men, six factors were evaluated. The Student's t-test revealed that there are significant differences; the Social Assistance Institutions present greater egalitarian attitudes as does the group of women. Meanwhile, the percentiles show these egalitarian attitudes at a medium level. The Analysis of Variance (ANOVA) to compare groups revealed regarding religion, there are significant differences between the Christian, the Catholic and the people who claim to have no religious beliefs; Catholics are those who present a more favorable attitude towards gender equality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".