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Record W4378650647 · doi:10.7202/1099982ar

Adjuncting for Life: The Gendered Experience of Adjunct Instructors in Ontario

2023· article· en· W4378650647 on OpenAlexaffvenueabout
Leslie Nichols

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMentorshipAdjunctWork (physics)Public relationsMedical educationPolitical scienceSociologyPsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

The market-based imperatives driving economic growth in Western societies have, in ways, both been acknowledged and implicitly used to reorient public institutions - academia dramatically so. This article deals with upending of post-secondary academic hiring priorities, and the impact on the adjunct or sessional lecturers implicated in the change. Over half of the courses offered by academic departments and programs in Ontario, Canada, are now taught by part-time faculty members (Pasma & Shakes, 2018). Their use in post-secondary education is underpinned by a notion of just-in-time course delivery in a free market of untenured PhD holders. This study assessed 26 adjuncts in Ontario, Canada, equally divided between male and female. It found working conditions, development of research dossiers, and health and work-life balance to be characterized by gendered differences and hardships. Although the hardships of post-PhD adjunct work have been abundantly documented, this work brings to light components of the experience that have not been previously studied, most significantly, its health effects and gender nature. It concludes with policy recommendations to support adjuncts in Ontario and beyond, including mentorship, longer term contracts, institutional research funding, extended health benefits, and affordable childcare.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.159
GPT teacher head0.360
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes3
Has abstractyes

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