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Record W2908600097 · doi:10.1080/13676261.2019.1569214

Conceptualizing the social and cultural organization of street life among young people experiencing homelessness

2019· article· en· W2908600097 on OpenAlexaffabout
Tyler Frederick

Bibliographic record

VenueJournal of Youth Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsSociologyYouth studiesPerspective (graphical)Gender studiesYouth cultureSet (abstract data type)

Abstract

fetched live from OpenAlex

Research on youth homelessness has documented the unique and complex ways that youth relate to one another on the street and engage with their social environments. This work draws from a varied set of concepts, including concepts from subcultural theory, the youth geographies literature, and Bourdieu’s practice theory. However, little work to date considers how to integrate these different conceptualizations and perspectives on how young people experiencing homelessness engage culture and organize themselves socially in the course of their time on the street. This paper explores this question by using interviews and fieldwork with a group of 39 homeless and street-involved young people in Canada. Through this analysis, I outline a multilayered and practice focused perspective that focuses on the active way that young people engage and navigate the diverse social and cultural landscapes of street life.

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)0.002
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 score0.213
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0110.028
Scholarly communication0.0080.004
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.402
Teacher spread0.329 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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