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Record W4226442551 · doi:10.5281/zenodo.6411264

Does Volunteering in a Language Learning Centre Help Non-Native English Speaking Students' Emotional Well-Being?

2022· article· en· W4226442551 on OpenAlexaboutno aff
Hilda Freimuth

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLinguistics

Abstract

fetched live from OpenAlex

Feeling connected as one navigates life as a post-secondary student is a challenge for many students, both domestic and international. Many non-native speakers of English may not feel a sense of belonging or social connectedness at university and have emotional and other needs hindering their adjustment and success at university. Institutions often have various opportunities for students to volunteer at events, support centres, and other university units. Self-access centres, such as language learning centres and writing centres, have become common at many institutions and often have many student volunteers, making these an ideal environment for research on students. This study took place in one of these centres at a university in Canada - a language learning centre. In this study, researchers used a mixed methods approach to explore student volunteer perceptions. Survey responses that related to the emotional well-being of student volunteers were highlighted for this study. The data from the survey were then cross-referenced with the transcripts of the focus group study for further confirmation. Findings indicate that the act of volunteering in the centre made an impact on student volunteers’ emotional well-being. It gave students a sense of belonging and the feeling that they were part of a greater community. It also helped reduce loneliness and build self-esteem.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.001

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicService-Learning and Community EngagementFrench-language works237,207