A comparison of best practices for doctoral training in Europe and North America
Bibliographic record
Abstract
The PhD degree was established in Berlin 200 years ago and has since spread across the whole world. While there is general agreement that the degree is awarded in recognition of successfully completed research training, there have been significant differences in the way doctoral training programs have developed in particular countries. There is, however, a clear global tendency to follow the programs currently used either in the United States or in Europe. To determine more clearly how US and European PhD programs are both similar and different, we have used a validated questionnaire to analyze biomedical PhD programs in four representative institutions at Vanderbilt University, University of Manitoba, Karolinska Institutet, and Graz Medical University. The analysis is based on 63 detailed questions concerning the research environment, outcomes, admission criteria, content of programs, mentoring (or supervising), the PhD thesis, assessment of the thesis, and PhD school structure. The results reveal that while there is considerable overlap in the aims and content of PhD programs, there are also considerable differences regarding the structure of PhD programs, mentoring and assessment of PhD theses. These differences are analyzed in detail in order to provide a foundation for discussion of their relative advantages and disadvantages, with a view to providing a platform for discussion of best practices. The results will be of importance in the continued development of global discussion about development of doctoral training.
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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 | 0.070 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".