Bibliographic record
Abstract
This dissertation is a collection of three chapters in Development Economics and Gender for the Mexican context. Chapter 2 analyses whether the gender composition of decision-making boards affects promotion decisions for either male or female researchers, by using a unique database for a context in which a group of peers makes all promotion decisions for all academic institutions in Mexico. The empirical analysis examines the probability of promotion for each researcher enrolled in the National System of Researchers, and how this is affected by the committee's gender composition, exploiting the random assignment of evaluators. The results show that women in decision-making committees do not significantly favor the probability of promotion for women; but women facing a male-only committee have a lower probability of promotion than men. Chapter 3 studies the effects on social attitudes of the sharp increase in violence experienced in Mexico during the "Drug War". This is done through a lab-in-the-field experimental approach with Mexican undergraduate students. The results suggest that there are experience-type specific effects for the different levels of violence exposure. Differential gender effects are also found; women with drug war-related violence experience appear to have two different behaviors; depending on which type of violence experience they had; one where they become community builders and show solidarity, and the other one where they develop a lot of fear and feelings of vulnerability and show spite. Chapter 4 studies the effect of the sharp increase in violence in Mexico on preventive health care attitudes, and on classic health measurements. The data used in this study is a match of the INEGI monthly homicide reports at the municipality level with the individual level data from the Mexican Family Life Survey. The results presented suggest that having high levels of violence can affect the individual's health when measured by classic variables such as blood pressure, hospitalizations, body mass index, and mental health; and it can also affect the behaviors that could help alleviate health problems, such as having a healthier lifestyle including non-smoking, spending time outdoors, sleeping well, going for wellness checkups, and having a positive mindset about oneself.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".