Bibliographic record
Abstract
Abstract Motivation In areas of the world where poverty and inequality are deep and pervasive and social protection systems comparatively fragile, cash transfers are becoming commonplace and often promoted by international institutions and aid agencies as a viable instrument for social protection. Particularly, conditional cash transfers (CCTs) are being looked to as a means of reducing poverty while also investing in human capital. Purpose To capture some of the main critiques of CCTs from conception to evaluation, while identifying both gaps and opportunities for research and consideration for the future of CCTs. Methods A rapid review process was used. The initial search was conducted using a number of online peer‐reviewed databases. The initial search process yielded 993 sources, results were then limited to full‐text, English language, and to sources published between 2008 and 2017. Sources were then screened. Finally, 44 articles were chosen for in‐depth review. Findings This review captures some of the main critiques of CCTs from conception to evaluation, while identifying both gaps and opportunities for research and consideration for the future of CCTs. Policy implication We discuss responsibilities and implications for social work professionals who may be involved in the design, implementation or evaluation of CCT programmes domestically or internationally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".