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
In this paper, I examine the role of household income in determining who bribes and how much they bribe in health care in Peru and Uganda. I find that rich patients are more likely than other patients to bribe in public health care: doubling household consumption increases the bribery probability by 0.2-0.4 percentage points in Peru, compared to a bribery rate of 0.8%; doubling household expenditure in Uganda increases the bribery probability by 1.2 percentage points compared to a bribery rate of 17%. The income elasticity of the bribe amount cannot be precisely estimated in Peru, but is about 0.37 in Uganda. Bribes in the Ugandan public sector appear to be fees-for-service extorted from the richer patients amongst those exempted by government policy from paying the official fees. Bribes in the private sector appear to be flat-rate fees paid by patients who do not pay official fees. I do not find evidence that the public health care sector in either Peru or Uganda is able to price-discriminate less effectively than public institutions with less competition from the private sector.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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