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Record W2993992514 · doi:10.47678/cjhe.v28i3.183318

Off the track: A profile of non-tenure track faculty at McGill University

2017· article· en· W2993992514 on OpenAlexaffvenueabout
Carol Cumming Speirs, Rhonda Amsel, Malcolm G. Baines, Jo-Anne Pickel

Bibliographic record

VenueCanadian Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMcGill University
Fundersnot available
KeywordsTrack (disk drive)Government (linguistics)Fast trackPower (physics)Political scienceSociologyDemographic economicsEconomicsEngineering

Abstract

fetched live from OpenAlex

A survey conducted at McGill University suggests that non-tenure track faculty are a diverse group of highly-qualified individuals whose employment status involves a high degree of uncertainty. In accordance with other Canadian and American studies, the survey also found that a disproportionate number of women occupy non-tenure track as opposed to tenured or tenure track positions. Since the 1980s, North American universities have responded to increasing student enrollments and con- tinued cuts to government funding by appointing significant numbers of faculty to full-time and part-time non-tenure track positions. Due to the precariousness of their employment status, non-tenure track faculty rep- resent an attractive buffer in times of financial restraint. Despite their increasing numbers, however, little is known about the composition and concerns of non-tenure track faculty as a group. This article describes this group at one university and puts into question the structural and power relations that have led to their increased use and abuse.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.037
GPT teacher head0.309
Teacher spread0.272 · 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 designObservational
DomainIncentives
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
Published2017
Admission routes3
Has abstractyes

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