Student Volunteer Motivations in a Student Support Centre for English for Academic Purposes Students
Bibliographic record
Abstract
The findings presented in this paper look at the motivations of volunteers who supported English for Academic Purposes students at a self-access Language Learning Centre at a university in Canada. It also importantly sheds light on the motivations of a less investigated aspect of volunteerism, that of non-native English speakers who provide support to English language learners. In this study, 90% of the volunteers were non-native speakers of English, with most being international students. The majority of the volunteers were also graduate students (90%). Thirty volunteers in total participated in the survey, with seven volunteers participating in the focus group study. The data gleaned from both the survey and the focus groups in terms of motivations were analyzed (the latter via a content analysis) and then placed into the categories of Clary et al.’s (1998) Volunteer Function Inventory. The analysis revealed that a strong motivating factor for many was career-related, with a secondary motive of learning through volunteering in the centre or of using previously unused skills at the centre. Additionally, 97% of the volunteer students surveyed stated their work at the centre was an opportunity to make new friends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".