Bibliographic record
Abstract
This chapter address the rise of research performance measurement as an instrument of governance designed to steer the higher education sector in a specific direction. Performance measurement is always a political decision and it is about both accountability and control. Performance measurement is directed at many different entities, it serves multiple purposes, and it represents a variety of goals and values. In order to focus on the level of convergence between nations in the use of performance measurement of research in higher education institutions, this chapter examines the range of stated purposes behind the decision to measure performance. The chapter address research performance measurement in Australia, Canada, and the UK, and focuses on assessing convergence in ‘talk’ about performance measurement by senior administrators. It seeks to uncover how performance measurement is labelled and represented in these countries, and to examine the level of similarity across these nations. Hence, performance measurement is an example of a governance instrument which is utilized to shed light on how the higher education sector is being steered in various locations.
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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 | 0.101 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.034 | 0.061 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".