Responding to the Field and to the Academy: Ontario's evolving PhD
Bibliographic record
Abstract
The knowledge economy has increased the demands on our university systems to create innovative, flexible doctoral programs. Some countries have responded to this challenge by developing professional doctorates. In the province of Ontario (Canada), the trend appears to be to re-invent the traditional PhD rather than to develop professional doctorates. This paper traces historical, political, economic and social reasons for this trend. It focuses, in particular, on the case of the longstanding Doctor of Education (EdD) at the Ontario Institute for Studies in Education of the University of Toronto (OISE/UT). Enrolment in the EdD program has dropped significantly in the past few years. Drawing on a variety of sources including evaluation data from PhD and EdD students, this paper examines reasons for this development. The authors conclude that the same climate that is fostering professional doctorates is also changing the landscape for PhD education, making the degree more responsive to the needs of educational stakeholders.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".