‘If you can call it a poem’: toward a framework for the assessment of arts-based works
Bibliographic record
Abstract
The use of artistic forms as an alternative means for representing research findings is gaining acceptance in the research community. There are, however, important yet unresolved and even contentious issues arising from these new applications of the arts. These include concerns about the level of expertise required to effectively utilize the arts in research, the appropriateness of various methods of creating artworks and the desirability of identifying criteria for assessing arts-based contributions. Centring on the question of criteria for the creation and assessment of arts-based works, we note that there are, at present, few salient guidelines. Drawing upon our experience in conducting a pilot project employing arts-based methods of representing research findings, we propose a Guiding Arts-Based Research Assessment (GABRA) meta-framework for assessing the quality and effectiveness of utilizing the arts for knowledge dissemination. This overarching framework incorporates normative, substantive and performative aspects of arts-based methods of representing research findings.
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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 | 0.204 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.036 | 0.014 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".