The Mexican <i>Oportunidades</i> Program: Questioning the Linking of Security to Conditional Social Investments for Mothers and Children
Bibliographic record
Abstract
The article examines the ways in which the Mexican Oportunidades program, a neo-liberal social investment program, is applied in Mexico. It comments on both the efficacy of the program and the ways in which motherhood and citizenship are redefined. The Oportunidades program is a conditional cash transfer program that provides money for nutrition and educational scholarships to poor mothers on condition that the funds are used toward enhancing their children's human capital development. These conditions are intended to lead to the eventual integration of the children into the market economy as skilled individuals. The article points out how the Mexican conditional cash transfer program has shifted the responsibility for social security from the state to the non-state sector, namely to mothers and families, banks and markets. The investment in children is done through mothers' regulated participation, based on conditions, and designed along gendered, cultural norms of mothering. I argue that the conditional cash transfer system attempts to insert mothers into the market as creditworthy consumers, yet it has inserted them into the economy and into political discourse not as citizens with rights, but as mothers with increased social responsibilities. Furthermore, even if mothers abide by all the conditions and children have access to additional years of schooling, the efficacy of this social investment model is called into question due to the instability of the Mexican economy, the precariousness of work, and high levels of poverty and inequality in Mexico.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".