Quality and trust: at the heart of what we do: a selection of papers from the 6th European Quality Assurance Forum
Bibliographic record
Abstract
This publication gathers together a representative sample of the contributions to the forum. The papers are as follows: European quality assurance in a global perspective: 'soft power' at work? / Mala Singh; Implementing quality assurance in doctoral education: a snapshot / Thomas Ekman Jorgensen; Quality assurance in comparison: Austria, Germany, Finland, United Kingdom, the United States of America and Canada / Andrea Bernhard; Putting quality at the centre of quality assurance: where is the centre? / Marion Coy; Perceptions of quality: [Norwegian Agency for Quality Assurance in Education] NOKUT's 'quality barometers for higher education': 2010 and 2011 / Jon Haakstad; Experiences gained from the implementation of quality management processes at a Greek higher education institution: cultural, organisational and stakeholder issues / P. Ipsilantis, N. Batis, D. Kantas, I. Papadopoulos and P. Trivellas; To understand and successfully utilise the learning outcome in higher education, must we first destroy it? / Ian Scott and Julian Martin; Building trust / Sjur Bergan.
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 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 | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".