La brecha laboral de género y la pandemia en la Ciudad de Buenos Aires
Bibliographic record
Abstract
More than a quarter of a century after the Fourth Women's Conference in Beijing (1995), the progress made in terms of gender equality and the labor market is uneven. The gender gap in employment rates, in the quality of jobs, in income and in participation in decision-making levels, was reduced very little. \n \nThe COVID-19 crisis shows the impact on women, due to their greater representation in some of the economic sectors most affected by the crisis. The reduction in employment and the exit of economically active women from the labor market were greater than that of men. \n \nThe contextual situation of the gender labor gap is investigated, using the usual indicators for its study in the window Q1 2015 - Q4 2021 with data from the GCBA Survey of Work, Occupation and Income (ETOI). The analysis is deepened and the situation in the quarters of its development (Q1 2019 - Q4 2021) is compared with the year prior to the start of the pandemic, through other indicators (average weekly hours worked and average income of the main occupation by sex and income gap according to position in the household, qualification, occupational category and registration in social security).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".