Bibliographic record
Abstract
Quality is not a random occurrence in arts and creativity partnerships. The achievement of quality must be planned. Any arts partnership requires both quality assurance and quality control components. Quality assurance manages quality of processes, while quality control measures deliverables - ‘products’ - against standards. This paper looks at quality from a global and local perspective and argues that arts experiences from children and young people need to be:
 • "Fit for purpose": i.e. The arts education experience should be suitable and relevant for the intended purpose and the intended participants/audience.
 • "Right first time": i.e. There are certain sets of attributes that are generally associated with quality arts engagement and arts partnerships within education and these can become ‘prerequisites’ for a programme, there by mistakes should be eliminated, or at least reduced.
 
 This paper outlines the key components of quality arts education and suggests that the focus of research needs to move forward from a impact of the arts to quality of the arts.
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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".