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Record W4253215146 · doi:10.32920/ryerson.14645088.v1

Social inclusion within community youth programming: an exploration of the experiences of first and second generation youth within mainstream, multicultural and ethno-specific organizations

2021· preprint· en· W4253215146 on OpenAlexaff
Marleah Beth Eriksson Graff

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of WinnipegToronto Metropolitan University
Fundersnot available
KeywordsMainstreamYouth studiesInclusion (mineral)Positive Youth DevelopmentMulticulturalismSocial exclusionSociologyFeelingPublic relationsGender studiesSocial psychologyPsychologyPolitical sciencePedagogyDevelopmental psychology

Abstract

fetched live from OpenAlex

This study explores the experiences of first and second generation immigrant youth within community youth programming in mainstream, multicultural and ethno-specific organizations. Through interviews with nine youth and three youth program staff this study reveals how youth view the community based programming they attend as well as how their experiences reflect social inclusion or exclusion. Young people's positive experiences are that youth programs are spaces that generate positive feelings, contribute to growth, assist in developing meaningful relationships and connect youth to their community. However, youth also disclose experiences which negatively impact their inclusion in youth programming. Using a lens of social inclusion, this study demonstrates the central role of community youth programming in creating socially inclusive or exclusive environments. These young people's recommendations for change provide solutions for making community youth programs more inclusive.

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0050.003
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.118
GPT teacher head0.317
Teacher spread0.199 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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