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Record W4399670370 · doi:10.1177/21582440241258222

Youth Risky and Antisocial Behaviors in Newfoundland and Labrador: The Perspectives of Young People

2024· article· en· W4399670370 on OpenAlexaffabout
Paul Alhassan Issahaku, Anda Adam, Alhassan Sulemana

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsGovernment of Newfoundland and LabradorMemorial University of Newfoundland
Fundersnot available
KeywordsIntersubjectivityPsychological interventionPsychologyExtant taxonSubjectivityQualitative researchSocial psychologyDevelopmental psychologySociologyEpistemology

Abstract

fetched live from OpenAlex

What do young people know about youth risky and antisocial behaviors (RASB) and what do they suggest could be done to address these behaviors? Although there is much literature on youth RASB, there has been little qualitative exploration of the question stated here. The current study aimed to broach the question and to fill the gap. The study contributes to extant literature by exploring types of RASB among youth, reasons for these behaviors, and possible ways to address them from the perspectives of young people in Newfoundland and Labrador (NL). Constructivist and interpretive perspectives where reality is determined through the social processes of subjectivity and intersubjectivity informed the study. Eighteen young people aged 15 to 24 years participated in three focus group discussions (FGD), and data were analyzed thematically. The three main themes resulting from the process were: (1) a spectrum of behaviors, which comprised six types of behaviors; (2) constructed explanations, where participants identified five possible reasons for RASB; and (3) suggested interventions, which comprised three subthemes on interventions to address youth RASB. The findings and their implications for further research and for policy and practice are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.981

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 studies0.0000.000
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 head0.018
GPT teacher head0.318
Teacher spread0.300 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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