The Changing Nature of Academic Work: Canadian Universities and the Flexible Firm Model
Bibliographic record
Abstract
Though much literature has been produced on the topic of academic restructuring, those works concerned with the Canadian context have mainly focused on issues of corporateuniversity linkages, the role of state coordination of public universities, and the disparity between funding and student enrollment. Very little work has been done in documenting or analysing the role of adjunct faculty, who now make up nearly half the university faculty, in Canadian universities. Statistics Canada has only once collected data on parttime faculty, and only one current analysis of this data has been conducted (Omiecinski, 2003). The Canadian Association of University Teachers, furthermore, only publishes data concerning fulltime faculty members. The implications of an emerging division between the use of fulltime and parttime faculty on the nature of academic work and the quality of postsecondary education has been yet unexamined. Drawing on labour market segmentation theory, this study presents the multiple ways in which the work of academic staff in Canadian postsecondary education has conformed to the principles of the flexible firm model, first observed of private business firms in the 1980s by John Atkinson. A series of semistructured interviews with academic faculty and administrators, as well as a collection of current secondary source data, informed the basis of this research. It was found that the changing nature of academic work in post secondary education is negatively affecting the quality of undergraduate education provided in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".