4 Academic Culture in Transition: Measuring Up for What in Taiwan?
Bibliographic record
Abstract
In this section the authors clearly show how rankings connect to the education industry, in particular in journal impact factors (IF) and a monopoly of academic publishers.Chuing Prudence Chou provides a case study of how the power of impact factors is experienced in Taiwan and what this means for the epistemic viability of regional knowledge, collegial relations, teaching, and community engagement.Heather Morrison demonstrates that university rankings and journal IF are interconnected business interests that have rapidly increased the cost of sharing knowledge and what is considered world-class knowledge.Ralf St. Clair analyses the impact of rankings on a university in Nigeria and a mid-sized university in Canada.In doing so he points to the need for nuance in understanding context but also the pressure on universities to operate with rankings in mind; for example, he shows how "up-voting" occurs.Universities that are mid-ranked can end up sliding down if they don't actively participate in the reputation game; they therefore work to build their reputation by narrowing their associations to institutions that can help them move up in reputation survey.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".