Bibliographic record
Abstract
Universities and nations have long recognized the direct contribution of graduate education to the welfare of the economy by meeting a range of research and employment needs. With the burgeoning of a global economy in the twentieth and twenty-first centuries, the economic outcome of doctoral education reaches far beyond national borders. Many doctoral programs in the United States and throughout the world are looking for opportunities to equip students to work in transnational settings, with scientists and researchers located across the globe. Nations competing within this global economy often have different and not always compatible motives for supporting graduate training. In this volume, graduate education experts explore some of the tensions and potential for cooperation between nations in the realm of doctoral education. The contributors assess graduate education in different systems around the world, including Australia, Brazil, Canada, Germany, India, Japan, Mexico, the Nordic countries, South Africa, the United Kingdom, and the United States. Many factors motivate the need for a global understanding of doctoral education, including the internationalization of the labor market and global competition, the expansion of opportunities for doctoral education in smaller and developing nations, and a declining interest among international students in pursuing their graduate education in the United States.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.045 | 0.028 |
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".