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Record W4303446559 · doi:10.7202/1091588ar

Control and Insecurity in Australian and Canadian Universities during the COVID-19 Pandemic

2022· article· en· W4303446559 on OpenAlexaffvenueabout
David Peetz, Sean O’Brady, Johanna Weststar, Amanda Coles, Marian Baird, Rae Cooper, Sara Charlesworth, Amanda Pyman, Susan Ressia, Glenda Strachan, Carolyn Troup

Bibliographic record

VenueRelations industrielles · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsRestructuringJob securityHigher educationPolitical scienceJob satisfactionHappinessPandemicPublic relationsWork (physics)Economic growthCoronavirus disease 2019 (COVID-19)PsychologyEconomicsMedicineSocial psychology

Abstract

fetched live from OpenAlex

This study examines how the COVID-19 pandemic and ensuing university management control strategies have influenced higher education workers’ job security, stress and happiness. The primary quantitative and qualitative data are drawn from a survey of fourteen universities across Australia and Canada, supplemented by secondary research. The analysis examines institutional and worker responses to the pandemic, and resulting conflict over financial control at the macro (sector), meso (university) and micro (individual) levels. At the macro level, university responses were shaped by public policy decisions at both national and subnational layers of the state, and the higher education sector in both countries had a distinctly neoliberal form. However, Australian universities were exposed to greater financial pressure to cut job positions, and Australian university management might have been more inclined to do so than Canadian universities overall. Different institutional support for unionism at the macro level influenced how university staff were affected at the meso and micro levels. Restructuring at the universities across both countries negatively impacted job security and career prospects, in turn leading to reduced job satisfaction and increased stress. Although working from home was novel and liberating for many professional staff, it was a negative experience for many academic staff. Our analysis demonstrates that the experiences of university staff were influenced by more than the work arrangements implemented by universities during the COVID-19 pandemic. The approaches of universities to job protection, restructuring and engagement with staff through unions appeared to influence staff satisfaction, stress and happiness. Our findings extend the literature that documents how university staff routinely challenge neoliberalization processes in a variety of individual and collective actions, particularly in times of crisis. We argue that theorization of struggles over control of labour should be extended to account for struggles over control of finance. Abstract We studied 14 universities across Canada and Australia to examine how the COVID-19 crisis, mediated through management strategies and conflict over financial control in higher education, influenced workers’ job security and affective outcomes like stress and happiness. The countries differed in their institutional frameworks, their union density, their embeddedness in neoliberalism and their negotiation patterns. Management strategies also differed between universities. Employee outcomes were influenced by differences in union involvement. Labour cost reductions negotiated with unions could improve financial outcomes, but, even in a crisis, management might not be willing to forego absolute control over finance, and it was not the depth of the crisis that shaped management decisions.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.352

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0200.007
Scholarly communication0.0060.002
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.071
GPT teacher head0.349
Teacher spread0.278 · 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.

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

Citations8
Published2022
Admission routes3
Has abstractyes

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