Towards the Use of the ‘Great Wheel’ as a Model in Determining the Quality and Merit of Arts-based Projects (Research and Instruction)
Bibliographic record
Abstract
Building upon a First Nations circle metaphor this paper explores how employing the interrelated concepts of pedagogy, poiesis, politics, and public positioning can provide a more holistic approach in designing and assessing arts-based projects be they for instructional and/or research purposes. It takes a ‘postmodern’ stance (Giroux, 1991), integrating Western and First Nations epistemologies to provide an organic framework that articulates how these and other concepts interrelate, providing a more inclusive model of assessment. First, it outlines a conceptual framework that follows Paula Underwood’s (2000) suggestion to use a “traditional medicine wheel for enabling learning and for gathering wisdom.” It then utilizes the constructed model to examine a few arts-based cases, indicating how each project will have its own particular emphasis within the various quadrants with unique characteristics.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".