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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".