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Record W2778570520 · doi:10.7910/dvn/et8wjd

Power vs Money: Alternative Approaches to Reducing Child Marriage in Bangladesh, a Randomized Control Trial

2018· dataset· en· W2778570520 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHarvard Dataverse · 2018
Typedataset
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsImmunoPrecise (Canada)
Fundersnot available
KeywordsControl (management)Randomized controlled trialPower (physics)EconomicsMedicineInternal medicineThermodynamics

Abstract

fetched live from OpenAlex

A clustered randomized trial in Bangladesh examines alternative strategies to reduce child marriage and teenage childbearing and increase girl's education. Communities were randomized into three treatment and one control group in a 2:1:1:2 ratio. From 2008, girls in treatment communities received either i) a six-month empowerment program, ii) a financial incentive to delay marriage, or iii) empowerment plus incentive. Data from 15,739 girls 4.5 years after program completion show that girls eligible for the incentive for at least two years were less likely to be married under 18, less likely to have given birth under 20, and more likely to be in school at age 22. Unlike other incentive programs that are conditional on girls staying in school, an incentive conditional on marriage alone has the potential to benefit out-of-school girls. We find insignificantly different effects for girls in and out of school at baseline. The empowerment program did not decrease child marriage or teenage childbearing. However, girls eligible for the empowerment program were more likely to be in-school.

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.007

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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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