A Model for the Regionalization of Higher Education: The Role and Contribution of Tuning
Bibliographic record
Abstract
A notable evolution in the internationalization of higher education in the last decade has been the increasing emphasis on regional level collaboration and reform initiatives. The purpose of this paper is to examine the process of regionalization through the lens of a conceptual model and to demonstrate how different Tuning initiatives serve as useful instruments in the application of the model, and the ultimate realization of higher education regionalization. The evolving nature and meaning of region and regionalization are explored in the first section of the paper. This leads to an analysis and conceptual mapping of the many terms used to describe the phenomenon. The proposed model is based on three distinct but complementary approaches; Functional, Organizational and Political Approaches (FOPA). The three approaches are inter-related. The model is generic in concept and purpose so that it can apply to the evolving process of higher education regionalization in different parts of the world. The article examines how the initiatives and implications of the Tuning process are directly related to the model and consequently make important contributions to the regionalization of higher education in all regions of the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".