¿Las mujeres y los niños primero? Nuevas estrategias de inversión social en América Latina
Bibliographic record
Abstract
espanolEn los ultimos anos, en America Latina se ha impulsado una serie de planes sociales, algunos de ellos enfocados en la ninez y la inclusion social. Sin embargo, aunque varias de estas iniciativas se tradujeron en importantes beneficios asociados a la reduccion de la pobreza, resultan claramente insuficientes a la hora de aliviar las cargas domesticas de las madres para mejorar su empleabilidad y, al mismo tiempo, generar espacios institucionalizados de aprendizaje y desarrollo infantil para los mas pequenos. Una verdadera politica de inclusion social requiere de la adopcion de medidas integrales, que contemplen la compensacion monetaria como asi tambien la inversion en servicios que permitan el desarrollo de capital humano. EnglishIn recent years, in Latin America, a series of social plans have been driven, Some of them focused in childhood and social inclusion. However, although various of these initiatives have been translated into important benefits associated with the reduction of poverty, they have clearly been insufficient when alleviating the domestic charge of mothers to improve their employability, and at the same time generating institutional spaces of learning and infant development for the oungest. One true policy of social inclusion requires the adoption of integral measures, which include monetary compensation as well as investment in services that allow for the development of human capital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".