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Record W4392235093 · doi:10.1080/13676261.2024.2321157

The social reproductive labour of university students with hostile Jobs

2024· article· en· W4392235093 on OpenAlexafffundabout
Kiran Mirchandani, Hongxia Shan

Bibliographic record

VenueJournal of Youth Studies · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British ColumbiaThe Wilson CentreUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologyLabour economicsGender studiesDemographic economicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This paper focuses on the social reproductive labour of one group of young people - university students who hold hostile jobs. Engaging in paid work while studying full-time has become common for university students. Despite the desire for high-quality work-integrated learning opportunities, many working undergraduate students face precarious working conditions in service sector jobs. Full-time undergraduate students at two Canadian universities who engaged in term-time paid work participated in focus groups, life mapping, interviews, and audio diaries. The data revealed that more than half of the students experienced hostile work that is characterized by precarious conditions, intensified working pace, erratic scheduling and discrimination. We explore three kinds of social reproductive labour done by young working university students in hostile jobs – the labour of navigating their job conditions, the labour of juggling work and study, and the labour of striving for control and well-being. Our analysis suggests the need to broaden the recognition of the work of working students and to ensure better quality work for young people. Conceptually, the paper is informed by feminist political economy, particularly debates on social reproductive labour.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.435
Teacher spread0.356 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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