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
Purpose In this paper teaching excellence awards are evaluated, with an eye to improving them. Design/methodology/approach Literature is reviewed and an analytic framework developed in Canada is modified to apply to the University of Sydney's Vice Chancellor Outstanding Teaching Award. Data come from 60 respondents familiar with the Sydney award and web research on the Australian Group of Eight research‐intensive universities. Findings Among the conclusions reached are that the Sydney award is supported even by those who have been unsuccessful in applying for it, that awards alone do not make teaching the equal to research in a university that identifies itself as a research university, awards that integrate into the university's strategic direction are powerful, and that awards that have a continuing profile ease that integration. Research limitations/implications Along the way, several contentious points are discussed including the relationship of awards to promotion and the importance of pedagogic awareness of the reflective practitioner in picking out outstanding teachers who can articulate their approach to benefit others and to integrate with the larger purposes of the university beyond their own classroom. Originality/value Some practical means to enhance the impact of teaching awards are identified.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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