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Record W4293589717 · doi:10.17645/si.v10i3.5504

When Family Policy Doesn’t Work: Motives and Welfare Attitudes Among Childfree Persons in Poland

2022· article· en· W4293589717 on OpenAlexfundno aff
Dorota Szelewa

Bibliographic record

VenueSocial Inclusion · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsThematic analysisGenerosityWelfareContext (archaeology)CashSocial policyWelfare stateSociologyPreferenceWelfare reformPublic policySocial psychologyPsychologyQualitative researchEconomic growthEconomicsPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

The primary goal of this article was to analyse the welfare attitudes of people self‐declaring as childless by choice alongside the exploration of their social experience as childfree persons in the context of a rapid increase in the generosity of pro‐natalist public policies in Poland. The analysis is based on semi‐structured interviews conducted with 19 respondents recruited via Facebook network groups. Thematic analysis was applied identifying six general themes: “satisfied and never had the need”; “dealing with social pressure”; “family measures—yes, but not this way”; “unfair treatment of the childfree”; “towards welfare state for all”; and “change my mind? Never, even if offered one million dollars.” The research demonstrated that childfree persons present favourable views on state support for families with children. While critical of cash‐based family support, respondents have a clear preference for investing in services enabling women to participate in the labour market. Finally, if public policies aimed at removing barriers to parenthood were strengthened, this would not change the respondents’ minds about procreation.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2022
Admission routes1
Has abstractyes

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