Training ‘Deep Practitioners’: 50 Years of the Center for International Education at the University of Massachusetts Amherst
Bibliographic record
Abstract
This chapter presents a brief analytic history of the initial 50 years of the Center for International Education (CIE) at the University of Massachusetts Amherst with the goal of understanding what made it possible and what can be learned from it for the future of Comparative and International Education programs in other universities. The chapter begins with the unusual context in which CIE was created and its commitment to a synergistic linkage between academics and managing funded, development education programs. The discussion then describes CIE’s defining characteristics, the challenges it faced, its current situation, and the insights that can be gleaned from its history. The chapter concludes with comments on the implications for the future shape of CE/IE graduate programs and centers at universities. The author is the Founding Director of CIE who has led the program for most of its 50-year history.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".