Encouraging Community Action Against Teacher Absenteeism: A Mass Media Experiment in Rural Uganda
Bibliographic record
Abstract
Chronic teacher absenteeism is widespread in Uganda, with approximately one-third of public school teachers absent on any given day. Absenteeism and other problems that arise in Uganda’s public education system are often attributed to a lack of public oversight and parental involvement. In an effort to develop a scalable method of encouraging community engagement on this issue, the present study assesses the extent to which entertainment-education videos increase willingness among Ugandans to take action against absenteeism. Working in collaboration with Ugandan screenwriters and local actors, we developed video dramatisations that depicted the problem of absenteeism and how parents mobilised to address it. We assess the persuasive effects of these dramatisations both under lab-like conditions, to gauge immediate effects, and in the field, to gauge effects two months and eight months after a placebo-controlled media campaign attended by over 10,000 Ugandans in 112 villages. Although the persuasive effects are weaker in the field than the lab setting, the former remain substantial even after eight months. The demonstrated ability of entertainment-education to change public views on this issue sets the stage for policy experiments that test whether entertainment-education campaigns have downstream effects on absenteeism and public school performance more generally.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".