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Record W2896842554 · doi:10.1177/0020715218807261

Constrained from leaving or comfortable at home? Young people’s explanations for delayed home-leaving in 28 European countries

2018· article· en· W2896842554 on OpenAlexvenueno aff
Andrew L. Breidenbach

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEurobarometerRentingDemographic economicsImmigrationWelfareEconomicsLabour economicsPolitical scienceEuropean unionLaw

Abstract

fetched live from OpenAlex

Many comparative studies of home-leaving examine behavior associated with this transition and the relative importance of both structural and cultural factors in helping or hindering it. Yet, we know surprisingly little about how youth understand these factors on a broad scale to be influencing home-leaving for their generation. This article compares young people’s beliefs across cultures about why late home-leaving occurs using Eurobarometer survey data from 28 countries. I incorporate comparative home-leaving literature with theories about attitudinal worlds of welfare and explanations for social problems to argue that modes of explanation for late home-leaving hinge on whether youth see external, structural causes preventing earlier leaving (constraint-oriented explanations) or internal, more culturally motivated causes that lead individuals to stay at home longer (choice-oriented explanations). Demographic and institutional conditions that capture aspects of nations’ home-leaving contexts, such as women’s mean age at childbirth and the robustness of labor and housing markets, significantly correlate with the prevalence of these explanations. Findings suggest that youth tend to perceive their generation’s housing exits as structurally limited by scarce housing and weak purchasing power. However, in richer countries with more effective employment markets and better access to rental housing stock, choice-oriented explanations are more popular.

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)0.000
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 score0.275
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.387
Teacher spread0.328 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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