A Bayesian cohort model for estimating out-of-school rates and populations
Bibliographic record
Abstract
The out-of-school rate is a critical indicator for monitoring global progress towards universal education. It quantifies the population of children and youth excluded from each level of the education system. As with many education indicators, historical out-of-school reporting has relied exclusively on imperfect administrative data. Recently, the education community has turned to survey data as a supplement to administrative data to overcome its gaps and weaknesses. Producing such consolidated estimates globally in the out-of-school rate context, however, is a challenging task due to the diversity in enrolment patterns, systematic differences in the nature and reliability of administrative and survey-based data, and the heavy presence of invalid administrative observations resulting from enrolment counts that exceed corresponding population estimates. In this paper we introduce a cohort-based Bayesian hierarchical model to address these challenges and produce complete time series of out-of-school rates for 192 countries. The model uses a flexible spline-based process for underlying cohort out-of-school rate curves that are smoothed through cohort progression and over time. Observations are related to these values using a dual likelihood setup where each data source has distinct bias and variance components. The administrative side includes a structure that propagates uncertainty information contained in invalid data to avoid understating uncertainty. Validation exercises and sensitivity analysis suggest that the model is reasonably well calibrated and offers a material improvement over simpler approaches. The model is currently used by UNESCO to monitor out-of-school rates for all countries with available data.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".