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Record W6892172987 · doi:10.5064/f6jkv8qz

Data for: Perceptions of Parental Functions among Married People in Benin

2021· dataset· en· W6892172987 on OpenAlexaff

Bibliographic record

VenueSyracuse University Qualitative Data Repository · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPerceptionConfidentialityNegotiationContext (archaeology)MisinformationFertilityAbortionListing (finance)Indonesian

Abstract

fetched live from OpenAlex

Project Overview This study aims to identify the perceptions of parenthood among people living in a married couple in Benin. The data come from a work to collect more information on the understanding of high fertility among married Beninese based on their social representations of the child, medical contraception, abortion as well as their perceptions of determinants of the negotiation of the use of medical contraception in the married couple. To do this, semi-structured individual interviews were conducted with 30 volunteers of both sexes aged 18 and over. Analysis of the data shows a gendered perception of parental roles exposing a paternal function that is distinct from maternal function. In the context of parenting support policies, this data could be useful to the public authorities and to the people and structures that intervene in this field. Data Overview 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 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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.147
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1470.028

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 head0.078
GPT teacher head0.355
Teacher spread0.277 · 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 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

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