Student Volunteer Work and Learning : Undergraduates’ Experiences and Self-reported Outcomes
Bibliographic record
Abstract
More than ever, university students are seeking work experiences to help them develop general and career-related skills before graduation (Holdsworth & Brewis, 2014). They gain this experience through unpaid or voluntary work as well as paid work. This report follows from our earlier report on undergraduate students’ paid work at UBC (Taylor, Raykov & Sweet, 2020), and explores students’ involvement in volunteer work, including their motivations for participating and its perceived benefits. Our research confirms that unpaid work often involves different motivations and has different benefits and challenges vis-à-vis paid work. It is also less visible than paid work and tends to be given less research attention. This report begins by identifying the kinds of unpaid work opportunities that are available for undergraduate students at UBC, before turning to a review of the literature on students’ unpaid work and our findings from surveys and focus group interviews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".