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Parent-Adolescent Storytelling in Canadian-Arabic Immigrant Families (Part 2): A Narrative Analysis of Adolescents' Stories Told to Parents

2014· article· en· W13047223 on OpenAlexaffabout
Lynda M. Ashbourne, Mohammed Faez Baobaid

Bibliographic record

VenueThe Qualitative Report · 2014
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNarrativeStorytellingPsychologyContext (archaeology)Narrative inquiryGrounded theoryIdentity (music)AutonomyAcculturationSocial psychologyDevelopmental psychologyQualitative researchGender studiesSociologyEthnic groupLiterature

Abstract

fetched live from OpenAlex

This paper is the second of two papers presenting the results of a qualitative analysis of interviews inviting Arabic-Canadian immmigrant adolescents and parents to reflect on the stories they tell each other in the context of everyday family life. The first paper provides the results of a Grounded Theory Methodology and proposes a substantive theory of intergenerational storytelling during adolescence. This paper augments these results by presenting Narrative Analysis of a separate part of the interview inviting adolescents to tell a story to the interviewer as if telling it to their parents. Based on the stories told by 10 adolescents (5 male, 5 female), this analysis provides an initial representation of how the broad projects of acculturation and collective identity, as well as changes in parent-adolescent relationships, are brought directly into parent-adolescent day-to-day interaction in the form of small stories. These small stories present teens as performing in their day-to-day lives, with friends and strangers, and in the face of challengers and strange or familiar circumstances. The stories provide a context in which parent-adolescent interactional voices are prominent, and wherein understanding of unusual events, co-construction of self and family identities, broader social influences, and autonomy/connection dialectics emerge.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.072
GPT teacher head0.428
Teacher spread0.356 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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