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Record W4386191567 · doi:10.1007/s12186-023-09333-y

Fitting work? Students speak about campus employment

2023· article· en· W4386191567 on OpenAlexafffundabout
Alison Taylor, Catalina Bobadilla Sandoval

Bibliographic record

VenueVocations and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWork (physics)Flexibility (engineering)Higher educationAttractivenessEquity (law)PsychologyPublic relationsPart-time employmentAcademic achievementPedagogySociologyPolitical scienceManagementEngineering

Abstract

fetched live from OpenAlex

Balancing part-time work and studies has become commonplace for university students in Canada and other countries where the costs of education have risen over time. While there is a substantial literature on the impacts of term-time work on studies, little has been written about campus employment programs, which are becoming more commonplace in North American universities. This paper addresses this gap by considering students' experiences in such a program at a western Canadian university. Focusing primarily on qualitative data from a longitudinal study, we examine the various reasons for the attractiveness of this program, which go beyond the promise of professional, career-related work experience. Our analysis draws on the academic literature on work-study roles, which examines whether term-time work has a more positive or negative effect on student outcomes as well as sociocultural literature that is more attentive to different contextual features of the work-study relationship. We find that university-sponsored jobs are highly valued by students for their workplace relationships, regulation, and flexibility. Positive relationships at work are facilitated by supervisors' recognition of students' academic priorities and opportunities to develop peer-support networks on campus. Other important features for students include the convenience of working where one studies, and the ability to build work schedules around academic schedules. However, the limited access to 'good' campus jobs raises concerns about equity.

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)0.003
metaresearch head score (Gemma)0.011
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0180.009
Scholarly communication0.0120.004
Open science0.0010.008
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0130.003

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 head0.037
GPT teacher head0.386
Teacher spread0.349 · 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

Citations9
Published2023
Admission routes3
Has abstractyes

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