Bibliographic record
Abstract
Edited by Michelle StackFor many institutions, to ignore your university's ranking is to become invisible, a risky proposition in a competitive search for funding.But rankings tell us little if anything about the education, scholarship, or engagement with communities offered by a university.Drawing on a range of research and inquiry-based methods, Global University Rankings and the Politics of Knowledge exposes how universities became servants to the rankings industry and its impact.Conceptually unique in its scope, Global University Rankings and the Politics of Knowledge addresses the lack of empirical research behind university and journal ranking systems.Chapters from internationally recognized scholars in decolonial studies provide readers with robust frameworks to understand the intersections of coloniality and Indigeneity and how they play out in higher education.Contributions from diverse geographical and disciplinary contexts explore the political economy of rankings within the contexts of the Global North and South, and examine alternatives to media-driven rankings.This book allows readers to consider the intersections of power and knowledge within the wider contexts of politics, culture, and the economy, to explore how assumptions about gender, social class, sexuality, and race underpin the meanings attached to rankings, and to imagine a future that confronts and challenges cognitive, environmental, and social injustice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.891 | 0.830 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".