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Record W4224111182 · doi:10.1080/09518398.2022.2061074

“I wish every day was Saturday”: Newcomer youth and program facilitators’ experiences of a community-based resettlement program during the COVID-19 pandemic in Montreal

2022· article· en· W4224111182 on OpenAlexafffundabout
Emilia Gonzalez, Mónica Ruiz‐Casares

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPandemicContext (archaeology)Coronavirus disease 2019 (COVID-19)Public relationsService-learningSociologyPositive Youth DevelopmentYouth engagementPerspective (graphical)PsychologyPedagogyPolitical scienceMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, many crucial services for youth shifted to online delivery. Yet, little is known about the processes of providing online support to newcomer youth from the perspective of the service users. Say Ça! is a community-based organization in Montreal that supports newcomer youth through language tutoring and cultural activities. Photo journals by six newcomer 12–17-year-olds and group interviews with 11 program facilitators explored how the pandemic affected the youth’s experiences participating in Say Ça!. Findings highlight key elements of online learning program delivery essential to the youth’s engagement during the pandemic. Notably, adopting a relationship-centered approach that strengthened one-on-one tutor-youth relationships and a youth-centered approach that offered a space of self-expression, academic support, and leisure parting from the youth’s interests. Strategies developed by community-engaged organizations are essential to develop adequate services that respond to the changing needs of their populations in the context of a crisis.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.190
GPT teacher head0.541
Teacher spread0.351 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

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