The Social Investment Perspective, Conditional Cash Transfer Programmes and the Welfare Mix: Peru and Bolivia
Bibliographic record
Abstract
Conditional Cash Transfer (CCT) programmes have spread across Latin America since the late 1990s. They constitute one of the major changes in social policy in Latin America in the last twenty years (Barrientos, 2009). This innovation has significantly influenced the welfare mix (Esping-Andersen, 1990). Those who examine the welfare mix from a feminist perspective (Orloff, 1996; Martínez, 2008) insist that it is necessary to take into account the gender consequences of changing this mix. Based on a qualitative analysis of CCT programmes in Peru and Bolivia, this article makes two arguments. First, CCT programmes demonstrate that instead of being purely liberal or even neoliberal, the actions of the state in the production of welfare are now grounded from the perspective of social investment. Second, in Peru and Bolivia, the gendered impacts of this new state orientation nonetheless reinforce maternalistic and coercive practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".