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Record W6990431112

Did Somebody Say Community? Young Peopleâs Critiques of Conventional Community Narratives in the Context of a Local Drug Scene

2013· article· en· W6990431112 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyNarrativeContext (archaeology)Value (mathematics)Drug userResource (disambiguation)Order (exchange)
DOInot available

Abstract

fetched live from OpenAlex

The language of community is ubiquitous in academic, public health, and policy discourse about drug using populations. Yet, it has been argued that in some settings, the parameters of “the drug user community” are far from self-evident. We undertook this ethnographic investigation to explore experiences and understandings of a “drug user community” (sometimes referred to more specifically as a “street youth community”) among young people entrenched in Vancouver’s inner city drug scene. Our findings revealed that in this context, conventional notions of community—that is, a social network characterized by commonality, mutual responsibility, solidarity, and/or stability—resonated with some youth. However, most questioned the value of membership within this community, in which what they had in common with other youth were ongoing experiences of poverty, marginalization, and social exclusion. Many felt membership in the drug user community precluded their ability to be responsible and productive citizens within the wider community of “mainstream society.” Experiences of resource deprivation and everyday violence on the streets led many participants to emphasize the limited possibilities for community among their peers. We argue that it is important to critically examine heretofore essentializing assumptions about the nature of inner city drug user or street youth communities in order to better understand young people’s needs and desires in these settings.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.309
Teacher spread0.269 · 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
Published2013
Admission routes1
Has abstractyes

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