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Record W3110866865 · doi:10.1007/978-3-030-54618-2_3

Beyond the National: How the EU, OECD, and World Bank Do Family Policy

2020· book-chapter· en· W3110866865 on OpenAlexaff
Jane Jenson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversité de Montréal
FundersH2020 European Research CouncilStockholms UniversitetTurun YliopistoUniversität HamburgErasmus Universiteit RotterdamUniversity of Kent
KeywordsPolitical scienceAction (physics)Security policyEuropean unionPolicy analysisPsychological interventionPublic administrationEconomic policyBusinessPsychologyComputer security

Abstract

fetched live from OpenAlex

Abstract In recent decades, numerous international organizations have adopted positions that use components of a policy frame familiar from family policy at the national level. They sought to advance one or more of three classic goals of that domain: stabilizing demography, ensuring income security, and supporting parents’ labor force participation. This chapter tracks the last several decades of policy action in three international organizations—the European Union, the OECD, and the World Bank. It documents the changing interventions of each organization that touch on these three goals, whether or not the organization claims to be committed to having family policy. The analysis focuses in particular on the expressed policy goal(s), the targets and policy instruments, and the policy frame used to justify each. The main finding is that despite different trajectories over time the three share processes leading to non-familialization via greater emphasis on individuals and often children.

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)0.016
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0050.011
Scholarly communication0.0160.009
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.066
GPT teacher head0.326
Teacher spread0.260 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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