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Record W2461506770 · doi:10.1057/9780230100640_18

Making Space for Youth: iHuman Youth Society and Arts-Based Participatory Research with Street-Involved Youth in Canada

2009· book-chapter· en· W2461506770 on OpenAlexaboutno aff
Diane Conrad, Wallis Kendal

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2009
Typebook-chapter
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen journalismThe artsYouth engagementYouth participationSpace (punctuation)PerceptionYouth studiesPublic spaceParticipatory action researchCriminologyPolitical scienceSociologyPublic relationsPsychologyGender studiesEngineeringLaw

Abstract

fetched live from OpenAlex

Research into the experiences of homeless or street-involved youth in Canada (CS/RESORS, 2001) paints a shocking picture of deprivation, lack of support, and victimization suffered by youth on a daily basis. Yet the dominant public perception of street-youth is that they are nuisances at best and dangerous criminals at worst. Rather than providing much needed social assistance for youth caught in dire circumstances, governments cut funding and pass laws that make their survival even more precarious. While further research into youths’ street-involvement would add to our understandings of relevant issues, in particular, research is needed that works toward concrete material improvements in the lives of youth. Participatory research that employs youth as co-researchers to investigate youths’ street-involvement is a potential vehicle for such engagement. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.006
metaresearch head score (Gemma)0.003
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 score0.073
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0330.015
Scholarly communication0.0080.002
Open science0.0030.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.300
GPT teacher head0.421
Teacher spread0.120 · 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

Citations42
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan US eBooksSame topicHomelessness and Social IssuesFrench-language works237,207