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Record W4224294923 · doi:10.1080/01425692.2022.2060798

Learning to walk the wire: preparing students for precarious life

2022· article· en· W4224294923 on OpenAlexafffundabout
Alison Taylor

Bibliographic record

VenueBritish Journal of Sociology of Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrecarityPrecarious workExpansiveVulnerability (computing)SociologyDiversity (politics)Higher educationNarrativeCompetition (biology)Work (physics)Gender studiesPolitical sciencePublic relationsEconomic growth

Abstract

fetched live from OpenAlex

University students today are preparing for a more precarious future than previous generations, and the global pandemic has exacerbated their sense of insecurity and vulnerability. Drawing on data from a longitudinal study of undergraduate students at a large Canadian university, this paper examines the narratives of working students, before and since the pandemic lockdowns began in early 2020. Narratives focus on students’ attempts to handle the diverse rhythms of multiple activities, and how they respond to precariousness in work, family, and academic studies. Findings illustrate intractable tensions within higher education between logics of competition and care, and between access and selectivity. Further, universities, as well as employers, can be seen to contribute to the precarity of students, and to a ‘crisis of care’ in society more generally. Our analysis suggests the need for a more expansive and generous vision for higher education, which recognizes and supports students in their diversity.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0250.020
Scholarly communication0.0130.007
Open science0.0020.009
Research integrity0.0040.009
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 head0.026
GPT teacher head0.382
Teacher spread0.357 · 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

Citations12
Published2022
Admission routes3
Has abstractyes

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