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Record W4402544274 · doi:10.46303/jcve.2024.29

Transforming the Canadian Policy Agenda for School-Based Prevention of Youth Homelessness: Research as Activism

2024· article· en· W4402544274 on OpenAlexaffabout
Rebecca Stroud Stasel, Mélina Poulin, Jacqueline Sohn, Jacqueline Kennelly

Bibliographic record

VenueJournal of Culture and Values in Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCarleton University
Fundersnot available
KeywordsTransformative learningStigma (botany)Psychological resilienceYouth studiesPolitical sciencePublic relationsPositive Youth DevelopmentPublic policySociologyPedagogyGender studiesPsychologySocial psychology

Abstract

fetched live from OpenAlex

Youth homelessness (YH) demands transformative changes in research, education, and public policy. Distinct from adult homelessness (AH), poorly addressed YH may lead to AH. Prevailing media narratives and policy communications perpetuate stigma and are unrepresentative of youth’s lived experiences, hindering the educational sector’s capacity to implement supportive measures in youth homelessness prevention. Schools are well poised to provide preventative and mitigative supports to address YH, yet the work intensification of educators has reached a point of fatigue, thus threatening support efficacy. We conceptualize research as activism and propose that policy can be engaged as a matter of social justice and a means to transform society via research and knowledge mobilization (KMb). Our Canadian environmental scan informs several studies in progress, which share goals to: prevent YH; reduce harms from intersectional issues to YH; and ameliorate conditions for resilience pertaining to youth in or at risk of homelessness. We call for a multi-pronged approach to engage stakeholders and the education sector in addressing this high-stakes issue disproportionately affecting underserved youth. Our findings chart the next steps of this research as activism cycle.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.124
GPT teacher head0.525
Teacher spread0.401 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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