The development of doctoral degree curriculum in England: Perspectives from professional doctoral degree graduates
Bibliographic record
Abstract
The paper investigates two issues surrounding the field of higher education leadership, particularly in doctoral education, namely the reasons why potential doctoral students decided to enroll in a professional doctorate instead of a traditional Doctor of Philosophy (PhD) and how did professional doctorate status enhance its graduates’ professional development and career promotion. Guided by the Social Cognitive Career Theory (SCCT), 20 professional doctorate graduates who are members of a professional association in London were invited as participants. The paper discovers that in the view of professional doctorate graduates, the program offers the flexibility for professionals to enjoy the rigorous education at doctoral level. Second, the curriculum of the professional doctorate allows graduates to apply both theories and practical applications into their current workplace directly. Third, the rich supports from lecturers enhancing the prosperity of professional doctorate graduates’ life experience. This study provides solid evidence and recommendation for university administrators, policy makers, organizational employers, and potential doctoral students in the United Kingdom and other Anglophone countries to understand the learning outcomes and gained skills of professional doctorates. Keywords: career counseling; doctoral student experience; professional doctorate; qualitative
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".