Academic Ranking—From Its Genesis to Its International Expansion
Bibliographic record
Abstract
Given the visibility and popularity of rankings that encompass the measurement of quality of post-graduate courses, for instance, the MBA (Master of Business Administration) or graduate studies program (MSc and PhD) as do global academic rankings—Academic Ranking of World Universities-ARWU, Times Higher / Thomson Reuters World University Ranking and TR-THE, QS World University Rankings-TopUniversities-QS. With the sharply increasing importance of academic rankings, we seek to answer the following questions: What is the origin of academic ranking? How did it evolve from a local initiative into an international one? How have the agencies responsible for academic ranking developed? Despite this evolution, what are the constraints that still exist? In this qualitative study, we opted for bibliographic and documentary research. The findings confirm the predominance of strategic value intertwined with the reductionism of knowledge in the context of research and publication and, the non-consensual process of the internationalization of higher education, being engendered by a global elite (chiefly of American origin) geared to serve their own interests.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".