Conditional Cash Transfer Programs and the Sustainable Development Goals: Problematizing the Empowering Potential of Conditional Cash Transfer Programs
Bibliographic record
Abstract
Abstract Purportedly in line with the Sustainable Development Goals’ (SDGs) commitment to end poverty and gender inequality by 2030, conditional cash transfer programs (CCTs) provide poor households with cash contingent on parents making human capital investments in their children. Advocates claim CCTs empower and so benefit women and girls. Critics worry the programs reinforce gendered expectations by tying social protection to “good mothering.” The aim of this paper is to assess whether CCTs are compatible with the SDGs’ stated aims with regards to Goal 5 on gender equality and empowerment. I argue that CCTs run contrary to the stated aims of SDG 5. CCTs rely on and perpetuate sexist ideology about women while simultaneously policing women’s behavior to ensure they fulfill the state’s conception of a “good mother.” Notwithstanding the potential benefits women receive from CCTs, the programs prevent the disruption of power relations by reinforcing norms that incentivize women to engage in self-subordinating ways in exchange for cash. Given that the programs re-entrench and police gender norms, CCTs thwart progress towards SDG 5 and so move us no closer to a gender equal world.
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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 | 0.010 | 0.011 |
| 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 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".