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Descriptive statistics of departments.

2023· dataset· en· W6923424107 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicDate Palm Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Descriptive statisticsQuarter (Canadian coin)Official statisticsTreatyHealth statisticsDomestic violence

Abstract

fetched live from OpenAlex

<div> In 1995, Colombia signed the first legally binding international treaty that criminalizes all forms of violence against women. Subsequently, the government took several steps to improve laws and policies, but the progress was slow. This study uses a differences-in-differences approach and Demographic and Health Survey data to estimate the impact of a renewed effort to reduce intimate partner violence (IPV), based on recommendations by the UN. To identify the effect of the national policies, it uses the fact that while the central government passes laws and formulates policies, it partly relies on departments (provinces) to implement them. Of Colombia’s 32 departments and Bogota D.C., approximately a quarter had some type of gender policy in place by 2011. The main finding is that self-reported intimate partner violence decreased from 20% to 16% between 2010 and 2015 in departments that had implemented IPV policies, while it remained at 19% in the others. </div>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.076
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0960.020

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 head0.119
GPT teacher head0.310
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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