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Record W4384341660 · doi:10.1080/19439342.2023.2229294

The effects of booster classes in protracted crisis settings: Evidence from Kenyan refugee camps

2023· article· en· W4384341660 on OpenAlexaff
Andrew Brudevold-Newman, Thomas de Hoop, Chinmaya Holla, Darius Isaboke, Timothy Kinoti, Hannah Ring, Victoria Rothbard

Bibliographic record

VenueJournal of Development Effectiveness · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsWorld University Service of Canada
Fundersnot available
KeywordsBooster (rocketry)AttendanceKenyaRefugeeClass sizePolitical scienceEconomic growthPsychologyMedical educationMedicinePedagogyEngineeringEconomics

Abstract

fetched live from OpenAlex

Students in protracted crisis settings often face a range of challenges which combine to yield low education outcomes. This paper presents the results from a randomised controlled trial of weekend and holiday booster classes for 7th and 8th grade girls in Kakuma refugee camp in Kenya, that aimed to improve girls’ education outcomes and increase transition rates from primary to secondary school. While qualitative results suggested numerous advantages of the booster classes, including more freedom to ask questions, smaller class sizes, and kinder teachers, the program did not yield statistically significant effects on learning outcomes, school attendance or noncognitive skills. Mixed-methods research suggests that the limited impacts may stem from implementation challenges including irregular booster class attendance and a lack of appropriate teaching materials. More broadly, the results show the importance of accounting for implementation challenges in the reporting of impact evaluation results.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.016
GPT teacher head0.339
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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