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Record W2258499544

Passing hope around: Youth messaging strategies for becoming drug-free

2012· article· en· W2258499544 on OpenAlexaboutno aff
Warren Whyte

Bibliographic record

VenueInternational Journal of Narrative Therapy and Community Work · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeSolidarityEnthusiasmPublic relationsSociologyFeelingPublishingMedia studiesPsychologySocial psychologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Collective narrative practice facilitates geographically separated groups of people to share their experience and wisdom in standing up to common problems. This article documents a particular collective narrative practice between a group of youth in prison at Burnaby Youth Custody Services and a group of youth in treatment for substance misuse at Peak House in Vancouver, Canada. The purpose of outlining this exchange of solution knowledges is to highlight certain practical and theoretical aspects of collective practices that were effective for the youth, in order to continue the narrative discussion for future practitioners. By assuming the youth had healing knowledges, by providing them with a relevant audience, and by offering them the opportunity to make a meaningful contribution to others; this writer was able to facilitate young people in sharing their own solutions with each other in mutual encouragement against a common social issue. Exchanging collective narrative documents with other youth seemed to cultivate a sense of self-determination towards therapeutic work, a feeling of solidarity and belonging with similar strugglers, and a sense of hope and enthusiasm that change is indeed possible.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.004
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.356
GPT teacher head0.459
Teacher spread0.103 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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