Contingent Faculty: A Comparison of Accredited Business Schools in the U.S. and Canada
Bibliographic record
Abstract
Over the past three decades, U.S. colleges and universities have undergone a significant shift in faculty composition. The last 30 years have seen a decline in tenured faculty, replaced by full-time and part-time contingent faculty on a non-tenure track. The share of non-tenured faculty (contingent faculty) increased from 33% in 1987 to 48% in 2021, while the share of full-time tenured faculty declined from 39% to 24% during the same period. Driven by external factors including financial constraints, industry demands, and changing accreditation standards, schools reevaluated their faculty composition and adopted new strategies to meet market needs (AAUP, 2024). In Canada, data showed that between 2005 and 2015, the number of full-time positions decreased by 10%. In 2016, tenured and tenure-track faculty comprised 46.4% of the full-time faculty (Statistics Canada, 2016). There were several reasons for the decline in the share of tenured and tenure-track faculty. These included decreasing funding, rising costs for tenured faculty, higher research expenses, fewer student enrollments, and stricter faculty tenure standards. The faculty composition at accredited business schools in the U.S. and Canada was compared. A portfolio approach to faculty, balancing tenure, tenure-track, clinical, and lecturer positions, strengthened higher education. While tenured faculty remain essential for knowledge creation, doctoral mentorship, and long-term institutional leadership, full-time non-tenured faculty—such as clinical faculty and lecturers—enhance experiential learning, industry collaboration, and curriculum flexibility. This hybrid faculty model meets the demands of higher education and maintains the relevance and competitiveness of colleges and universities. Institutions must invest in contingent faculty to support goals related to impactful research, experiential learning, and teaching innovations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".