Reduccion de errores, fraude, y corrupcion en los programas de proteccion social
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social Protection (SP) and Social Safety Net (SSN) programs channel a large amount of public resources, it is important to make sure that these reach the intended beneficiaries. Error, fraud, or corruption (EFC) reduces the economic efficiency of these interventions by decreasing the amount of money that goes to the intended beneficiaries, and erodes the political support for the program. While no program is immune to EFC, evidence from developed countries demonstrates that such leakage can be brought to negligible levels. In five Organization for Economic Co-operation and Development (OECD) countries (UK, Canada, Ireland, New Zealand, and USA) this fraction is between 2-5 percent for the SP sector as a whole. For SSN programs, which use more complex eligibility criteria and hence are more prone to EFC, this fraction is 10 percent. To achieve these results, programs have implemented a number of measures reviewed in this note. In contrast, efforts to combat or even measure EFC are quite rare in developing countries, although some programs are plagued by it.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it