Bibliographic record
Abstract
drain' academic employment contracts 184, 294-5, 297, 318, 399, 461 academic freedom Erasmus 268 higher education in South Africa 207, 217 higher education in the USA 356, 358 neoliberalism impacts 378-9 research assessment impacts 368, 375-6, 377, 378 rights 375-6, 379 world class science 423 academic freedom defi cits 193, 251-2, 418, 419-20, 428, 429 academic misbehavior 362, 364, 368, 375 academic mobility 242, 248-9, 259, 260, 296-7, 300, 302, 336, 420, 471 academic quality assurance institutions globalization 438-40 international institutions 440-42 legitimacy of the global governance of academic quality 448-50 strengths and weaknesses of the global institutional framework 443-8 academic recruitment 72, 307-8, 319, 503, 504-5, 506, 510 academic standards see academic quality assurance institutions; quality/quality assurance; standards academic work practices 505-6 accountability global rankings 498, 503, 510, 511-12 globalization 203, 241 higher education in Canada 235 higher education in South Africa 204, 207, 208, 209-10, 215-16, 218 higher education in South Korea 288, 299, 301 neoliberalism 367-8, 375 new public management 199, 215 public choice theory 361, 363, 364 quality assurance 198, 199, 201, 231-2, 449 teaching 367, 368 types 367-8 see also quality/quality assurance; research assessments and the impact of research accreditation 231-6, 237, 278, 291-2, 311, 313-14, 337, 351-2, 356 actors, higher education steering 456 see also international actors Africa 45, 47, 53-4, 130, 132, 136, 139, 141, 144, 480, 506 see also South Africa; South African higher education reform; Sub-Saharan Africa agencies 457 see also intergovernmental academic quality assurance agencies; international organizations; national academic quality assurance agency networks AHELO (Assessment of Higher Education Learning Outcomes) 106-7, 441, 448 Altbach, P.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.739 | 0.562 |
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".