A Survey of Sessional Faculty in Ontario Publicly-Funded Universities
Bibliographic record
Abstract
Within the past decade, the unprecedented growth in non-tenure/tenure track faculty has led to speculation as to the learning environment and learning outcomes for students. Both national media and researchers have raised concerns about the growth in short-term contract faculty, yet there is little evidentiary data to support policy development. Our study of sessional faculty in Ontario’s publicly funded universities provides much needed data and insight into the current pressures, challenges, and adaptations of the rapidly rising number of university instructors who work on short-term contracts, also known as sessional faculty. From 2015 to 2016, our team of researchers reached out to 17 universities in Ontario and were able to conduct this study at 12 institutions across the province. Our team approached each institution or union/faculty association representing sessional instructors and asked them to distribute the survey instrument to all part-time, non-full-time, non-tenure-track instructors by email. The response rates ranged from 16% to 48% by institution, though notably we were sometimes only able to obtain estimates of the total number of questionnaires that were distributed because of email list issues. We reached out to roughly 7814 instructors and achieved an overall response rate of 21.5%. However, due to the lack of demographic data available on the whole population we are unable to determine the representativeness of the respondent population. For example, because this sample represents only those who have worked within the previous few years at the institution and where there is current contact information available to the institution or union/ association representatives, our email invitation may not have reached the full population of contract faculty at each institution. In order to provide clarity and context, qualitative data were obtained through interviews with 52 instructors who volunteered to participate selected from six institutions. The interview data is still being analyzed and will be presented in a subsequent reports and publications.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".