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Record W2142769778 · doi:10.1177/0743558407310733

Tough Teens

2008· article· en· W2142769778 on OpenAlexaffabout
Raewyn Bassett, Brenda L. Beagan, Svetlana Ristovski‐Slijepcevic, Gwen E. Chapman

Bibliographic record

VenueJournal of Adolescent Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Educational Sciences
Canadian institutionsUniversity of British ColumbiaDalhousie University
Fundersnot available
KeywordsInterviewConversationPsychologySemi-structured interviewSocial psychologyDevelopmental psychologyMedical educationQualitative researchSociologyMedicineSocial scienceCommunication

Abstract

fetched live from OpenAlex

Encouraging a teenager to have a conversation in a semistructured research interview is fraught with difficulties. The authors discuss the methodological challenges encountered when interviewing adolescents of European Canadian, African Canadian, and Punjabi Canadian families who took part in the Family Food Decision-Making Study in two regions of Canada. The researchers were interested in how family members made decisions about food choices. In all, 47 adolescents from 36 families agreed to an interview. The authors found recruitment of teens, locating a quiet space for interviews, the silencing effects of the tape recorder, and asking about abstract concepts to be constraints on adolescents' conversational abilities. Although each interviewer encountered many of the same challenges, some of those challenges played out differently in different ethnocultural groups. This article intersperses discussion about the challenges encountered with the four interviewers' reflections on their interviews with teens and with data from the interviews.

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)0.002
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0410.008

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 head0.454
GPT teacher head0.541
Teacher spread0.087 · 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

Citations116
Published2008
Admission routes2
Has abstractyes

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