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Record W3185279991 · doi:10.14288/1.0395094

Student Volunteer Work and Learning : Undergraduates’ Experiences and Self-reported Outcomes

2020· article· en· W3185279991 on OpenAlexaff
Milosh Raykov, Alison Taylor, Sameena Jamal, Sirui Wu

Bibliographic record

VenueOAR@UM (University of Malta) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVolunteer workPsychologyMedical educationWork (physics)PedagogyExperiential learningMathematics educationPublic relationsMedicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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