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Record W1895874052 · doi:10.15453/0191-5096.3560

The Economics of Being Young and Poor: How Homeless Youth Survive in Neo-liberal Times

2010· article· en· W1895874052 on OpenAlexaffabout
Jeff Karabanow, Jean Hughes, Jann Ticknor, Sean A. Kidd, Dorothy Patterson

Bibliographic record

VenueThe Journal of Sociology & Social Welfare · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental HealthDalhousie University
Fundersnot available
KeywordsYouth workIngenuityCitizenshipSociologyContext (archaeology)Work (physics)HarassmentPsychological resiliencePublic relationsGender studiesCriminologySocial psychologyPolitical sciencePsychologyLawPolitics

Abstract

fetched live from OpenAlex

Based upon in-depth interviews with 34 youth in Halifax and seven service providers in St. John's, Montreal, Hamilton, Toronto, Winnipeg, and Calgary, the findings of this study suggest that labor occurs within a particular street context and street culture. Formal and informal work can be inter-related, and despite the hardships they experience, young people who are homeless or who are at-risk of homelessness can respond to their circumstances with ingenuity, resilience and hope. Often street-involved and homeless young people are straddling formal and informal work economies while mediating layers of external and internal motivations and tensions. The reality is that the participants in this study cannot very easily engage in formal work. There is a dearth of meaningful formal work available, and when living homeless there are many challenges to overcome to maintain this work. In addition, there are few employers willing to risk hiring an individual who is without stable housing, previous employment experiences and, most likely, limited formal education. Therefore, street youth are left with informal work that provides them with survival money, basic needs, and a sense of citizenship, but which also invites belittlement, harassment, and mockery.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.022
GPT teacher head0.331
Teacher spread0.308 · 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.

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

Citations47
Published2010
Admission routes2
Has abstractyes

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