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Record W2002402862 · doi:10.1080/07294360220144114

Responding to the Field and to the Academy: Ontario's evolving PhD

2002· article· en· W2002402862 on OpenAlexaffabout
Catherine M. Allen, Elizabeth M. Smyth, Merlin Wahlstrom

Bibliographic record

VenueHigher Education Research & Development · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVariety (cybernetics)Professional developmentSociologyPoliticsField (mathematics)Political scienceHigher educationPedagogyEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.833

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0200.010
Scholarly communication0.0100.003
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.167
GPT teacher head0.376
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2002
Admission routes2
Has abstractyes

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