Toward a Global PhD?: Forces and Forms in Doctoral Education Worldwide
Bibliographic record
Abstract
AcknowledgmentsIntroduction / Maresi Nerad, Thomas Trzyna, and Mimi Heggelund I. Doctoral Education in Europe 1. Germany / Barbara M. Kehm 2. United Kingdom / Howard Green 3. Nordic Countries / Hans Kristjan Gudmundsson 4. The European University Institute / Andreas C. Frijdal 5. The Bologna Process / Jeroen Bartelse and Jeroen Huisman II. Doctoral Education in Africa, South America, and Mexico 6. South Africa / Ahmed Bawa 7. Brazil / Renato Janine Ribeiro 8. Mexico / Armando Alcantara, Salvador Malo, and Mauricio Fortes III. Doctoral Education in Australasia 9. Australia / Terry Evans, Barbara Evans, and Helen Marsh 10. Japan / Shinichi Yamamoto 11. India / Narayana Jayaram IV. Doctoral Education in North America 12. Canada / Garth Williams, with the collaboration of Martha Crago, Jonathan C. Driver, Louis Maheu, and Marc Renaud 13. United States of America / Maresi Nerad Conclusion / Maresi Nerad and Thomas Trzyna Appendix A: Past Differences, Current Commonalities, and Future Trends in Doctoral Education in Selected CountriesAppendix B: Seattle Declaration, September 2005ContributorsIndex
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".