A Model for the Regionalization of Higher Education: The Role and Contribution of Tuning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it