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Record W4398450119 · doi:10.7910/dvn/duba3j

Raw and Replication Data for: 'From Proof of Concept to Scalable Policies' and 'Mainstreaming an Effective Intervention'

2017· dataset· en· W4398450119 on OpenAlexaff
Abhijit Banerjee, Rukmini Banerji, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, James Berry, Michael Walton

Bibliographic record

VenueHarvard Dataverse · 2017
Typedataset
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsASTER
Fundersnot available
KeywordsReplication (statistics)MainstreamingIntervention (counseling)Proof of conceptRaw dataScalabilityComputer scienceMathematicsDatabasePsychologyProgramming languageMathematics educationStatisticsOperating systemSpecial education

Abstract

fetched live from OpenAlex

Raw datasets and programs creating tables for the papers "From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application" and "Mainstreaming an Effective Intervention: Evidence from Randomized Evaluations of 'Teaching at the Right Level' in India."

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)0.017
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.841
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.126
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.010
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0050.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1590.091

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 head0.052
GPT teacher head0.390
Teacher spread0.339 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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