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Record W2601661687 · doi:10.1177/0044118x17697236

Adolescent Civic Engagement and Perceived Political Conflict

2017· article· en· W2601661687 on OpenAlexfundno aff
Laura K. Taylor, Dana Townsend, Christine E. Merrilees, Marcie C. Goeke‐Morey, Peter Shirlow, E. Mark Cummings

Bibliographic record

VenueYouth & Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentQueen's UniversityUlster UniversityQueen's University BelfastUniversity of Notre Dame
KeywordsCivic engagementPoliticsSocial psychologyPsychologyPolitical socializationSociologyCriminologyPolitical scienceAmerican political scienceLaw

Abstract

fetched live from OpenAlex

Adolescents are often exposed to the lasting effects of political conflict. Complementing existing research on negative outcomes in these settings, this article focuses on the role of the family ( N = 731 mother/adolescent dyads, 51% female, M = 14.72, SD = 1.99, years old at Time 1) in promoting constructive youth outcomes in response to perceived conflict in Northern Ireland. Exploratory factor analyses revealed two related forms of youth civic engagement: volunteerism and political participation. Structural equation modeling revealed that compared with males, female adolescents reported more volunteerism. Older adolescents reported higher political participation and lower volunteerism. Moreover, over three time points, the primary model test revealed that the impact of perceived political conflict on adolescent volunteerism and political engagement was partially mediated by family cohesion. These findings suggest that amid protracted political conflict, the family may be a key factor underlying adolescents’ contributions to post-accord peacebuilding.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.347
Teacher spread0.244 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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