Bibliographic record
Abstract
Since the 1990s, internationalisation has become key for institutions wishing to secure funding for higher education and research. For the academic community, this strategic shift has had many consequences. Priorities have changed and been influenced by new ways of thinking about universities, and of measuring their impact in relation to each other and to their social goals. Debates are ongoing and hotly contested. In this collection, a mix of renowned academics and newer voices reflect on some of the realities of international research partnerships. They both question and highlight the agency of academics, donors and research institutions in the geopolitics of knowledge and power. The contributors offer fresh insights on institutional transformation, the setting of research agendas, and access to research funding, while highlighting the dilemmas researchers face when their institutions are vulnerable to state and donor influence. Offering a range of perspectives on why academics should collaborate and what for, this book will be useful to anyone interested in how scholars are adapting to the realities of international networking and how research institutions are finding innovative ways to make North�South partnerships and collaborations increasingly fair, sustainable and mutually beneficial.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".