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Record W3211258495 · doi:10.1163/25895745-03010003

Youth Employment Policies: Tackling Meanings and Social Norms within National Contexts

2021· article· en· W3211258495 on OpenAlexaffabout
María Eugenia Longo

Bibliographic record

VenueYouth and Globalization · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEmployabilityRealmSolidarityPublic policyContext (archaeology)SociologyState (computer science)Political sciencePolicy analysisAction (physics)Youth studiesPublic relationsPublic administrationGender studiesPolitics

Abstract

fetched live from OpenAlex

Abstract The transnational urgency of tackling youth employment problems has prompted state interventions, which have strongly geared youth policies toward employability. Applying a cognitive and interpretative approach, this article compares youth employment policies in four contexts—France, Canada, Quebec and Argentina—to highlight frames of reference and social norms involved in public action. The results reveal, first, commonalities and differences in public-policy approaches, in terms of goals, targeted populations, solutions, services and tools. Second, beyond policies’ formal characteristics, semantic analysis highlights the major national references and policy directions in the realm of youth employment. Third, the frames of reference show social norms shaping state solidarity and young people’s role in the labour market. The results stem from a documentary analysis of some 100 youth employment policies and programs, as well as interpretative analysis of interviews (N = 20) with experts and coordinators of some of the main policies in each context.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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 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

Citations6
Published2021
Admission routes2
Has abstractyes

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