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Record W3092066116 · doi:10.14288/bctj.v5i1.314

An Insider View: Understanding Volunteers’ Experiences Within a Peer-to-Peer Language Learning Program in Vancouver’s Downtown Eastside

2018· article· en· W3092066116 on OpenAlexaffabout
Natalia Balyasnikova

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsYork University
Fundersnot available
KeywordsDowntownInsiderPeer reviewPsychologyMedical educationMedicineGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Many community-based English language learning programs rely on volunteers to lead classes. While some of these volunteers have some teacher training, the majority are not professional educators. The question of how non-professionals understand what constitutes facilitation of language learning in an adult education context remains underexplored. This paper presents the findings of a small-scale study conducted within a community-based language learning program with four volunteer facilitators. Volunteer facilitators were interviewed on a range of topics related to their role in the program, peer-to-peer interaction, and the impacts of volunteering in their lives. An analysis of facilitator interviews, with reference to program’s guiding educational principles, reveals the following positive factors related to the program: the informal nature of the community, the flexible design of the program, peer-to-peer interaction, and support from program staff. However, the findings also highlight that facilitators’ perspectives and practices varied significantly due to their different lived experiences, motives for volunteering, and linguistic background. This study highlights promising practices, which could serve to design sustainable community-based English language learning programs for adults.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.313
GPT teacher head0.583
Teacher spread0.270 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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