Drivers and interpretations of doctoral education today: national comparisons
Bibliographic record
Abstract
In the last decade, doctoral education has undergone a sea change with several global trends increasingly apparent. Drivers of change include massification and professionalization of doctoral education and the introduction of quality assurance systems. The impact of these drivers, and the forms that they take, however, are dependent on doctoral education within a given national context. This paper is frontline in that it contributes to the literature on doctoral education by examining the ways in which these global trends and drivers are being taken up in policies and practices by various countries. We do so by comparing recent changes in each of the following countries: Canada, Colombia, Denmark, Finland, the UK, and the USA. Each country case is based on national education policies, policy reports on doctoral education (e.g., OECD and EU policy texts), and related materials. We use the same global drivers to examine educational policies of each country. However, depending each national context, these drivers are framed in considerably different ways. This raises questions about (1) their comparability at a global level and (2) the universality of the PhD. Also we find that this global-local nexus reveals unresolved tensions within the national doctoral educational frameworks.
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 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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".