The reluctance of scientists to engage in peer review of teaching: Finding the way forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it