The Impact of Degree-granting Requirements on Faculty Hiring Policies and Practices: A Case Study of Ontario's ITALs
Bibliographic record
Abstract
The purpose of this study was to explore the influence of the requirements of the Postsecondary Education Assessment Board (PEQAB) for graduate credentials for faculty teaching in college degree programs and the impact on hiring policies and practices at Ontario’s five Institutes of Technology and Advanced Learning (ITALs) which are part of the 24 Ontario Colleges and Applied Arts and Technology (CAAT). The Innovative Hiring Practices model developed by Jones-Kavalier and Flannigan (2008) served as a framework for identifying relevant concepts, questions, and data collection strategies that were used in this study. This study used the exploratory descriptive case study method to analyze data collected from ITAL websites and from interviews with 16 key informants who are most directly involved in the recruitment and hiring process at the five ITALs. Interviews were conducted with five Human Resource Managers (one from each ITAL) and eleven Deans responsible for programming in business, media studies, applied technology and liberal arts across the five ITALs. The conclusions drawn from this study suggest that there a number of policies and practices that enhance or deter from hiring the most appropriate faculty across all credentials - degrees, diplomas, and certificates - at the participating ITALs. The review of the relevant documents posted on the ITAL websites revealed a lack of clarity for potential applicants around the definition of the institutions referred to variously as ITALs, polytechnics, or colleges. Job descriptions that describe faculty roles did not capture the full essence of the position, the importance of teaching or the role of research. Attracting sufficient numbers of appropriate candidates to meet the Postsecondary Quality Assessment Board (PEQAB) requirements was identified by study participants as a serious concern. The constraints of the Academic Employees Collective Agreement on faculty utilization was perceived by participants as a substantive challenge for recruiting faculty with the appropriate expectations and expertise to facilitate student learning in the unique context of Ontario colleges. The study findings may inform policy and practice that would enhance the quality of work-life for faculty teaching in the degree programs in the five ITALs, for the ultimate benefit of student learning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".