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Record W6939498582 · doi:10.6084/m9.figshare.17694698

Encouraging Community Action Against Teacher Absenteeism: A Mass Media Experiment in Rural Uganda

2021· article· en· W6939498582 on OpenAlexaff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAbsenteeismMass mediaAction (physics)Test (biology)Intervention (counseling)CurriculumCommunity education

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0380.000

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.

Opus teacher head0.099
GPT teacher head0.344
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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