The reluctance of scientists to engage in peer review of teaching: Finding the way forward
Bibliographic record
Abstract
Over the last two decades universities globally have responded to a growing demand for higher education and hence the number and diversity of university students has increased dramatically (Bradley, Noonan, Nugent, & Scales, 2008; Universities Australia, 2013). At the same time publicly funded universities have faced decreasing budgets leading to radical changes in the delivery of education. There is an ever increasing push towards efficiencies through online learning and larger classes. Concomitantly governments have adopted a quality agenda in which universities are ranked against each other on the basis of teaching, leading to increased competition for recruiting quality students (TEQSA, 2011). As such considerable effort is being exerted by university managements and government agencies to define and measure quality teaching and learning standards (Coates, 2010; Kraus, Barrie, & Scott, 2012; Newton, 2002). However, as Newton points out, there are many interpretations of the meaning of quality, with academics and managers viewing the term differently.
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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 | 0.185 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".