Northeast Universities Development Consortium Conference HEC Montreal
Bibliographic record
Abstract
Abstract: This paper presents comparable national estimates of provider absence at primary schools and primary health centers in six countries. It relies on new data drawn from nationally representative samples of facilities using a common survey instrument and methodology, with providers counted as absent when they were not present in the facility at the time of an unannounced visit. Absence ranges from 11 to 27 percent among primary-school teachers, and from 23 to 40 percent among medical personnel. Absence rates are generally higher in poorer countries and states, with an additional $1000 in per-capita income (PPP-adjusted) reducing predicted absence by 2.7 percentage points. Absence generally does not appear to be concentrated among a small number of frequently absent providers, but instead is spread out over most providers, suggesting a general culture of tolerance for absence. Correlates of teacher absence include poor school infrastructure, which suggests that working conditions matter for absence, and distance from the nearest Ministry of Education office, which suggests that administrative monitoring may also be important. By contrast, proxies for salary levels, intensity of community monitoring, and intrinsic motivation levels are not robust predictors of absence.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.378 | 0.076 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".