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Record W6929563874 · doi:10.5064/f6wblx4i

Data for: Negotiation of the Use of Medical Contraception: Levers and Obstacles within Married Couple in Benin

2021· dataset· en· W6929563874 on OpenAlexaff

Bibliographic record

VenueSyracuse University Qualitative Data Repository · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsNegotiationFertilityData collectionConfidentialityParticipant observationFamily planningListing (finance)Abortion

Abstract

fetched live from OpenAlex

<h3>Project Overview</h3> This study aims to identify the factors that influence the negotiation of the use of medical contraception within married couple in Benin. It is an integral part of a larger data collection intended to understand the high fertility in Benin through the social representations of the child, medical contraception, and abortion as well as the determinants of the negotiation of medical contraception among married people. To do this, semi-structured individual interviews were conducted in Benin in the period from February to March 2018 with 30 married people of both sexes. The analysis of these interviews made it possible to observe that the negotiation of the use of medical contraception in the married couple is influenced by incentive factors, limiting factors and a factor having an ambivalent role. The data collection was carried out in French and in local language Fon. <h3>Data Overview</h3> The shared data consist of a table listing the codes employed, their definitions, and relevant excerpts. The full transcripts cannot be shared due to participant confidentiality protections.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.047
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.182
GPT teacher head0.359
Teacher spread0.177 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

Explore more

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