What Works? The Culture of Evidence in University Teaching
Bibliographic record
Abstract
This article analyses the culture of evidence in university teaching and its implications in the professional training of teachers in higher education. The new culture of organisation and assessment introduced into university teaching has brought about the configuration of a management model geared towards results and accountability based on solid evidence. Its implementation means that both administrators and teachers are asking themselves: what works? This study shows that the implementation of a culture of evidence requires the adoption of a pluralist vision of evidence, as well as clear criteria for determining the validity of evidence. In addition, teachers should be trained to mobilise systematic pedagogic knowledge and transform their practice, using available institutional support, the systematic analysis of their own experience, and the promotion of best practice.
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.
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 | 0.402 | 0.597 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.012 | 0.077 |
| Scholarly communication | 0.063 | 0.040 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.012 |
| 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".