AN ARTS-BASED CURRICULUM ENCOUNTER: WHAT DOES IT MEAN TO LIVE ON THIS LAND?
Bibliographic record
Abstract
Our arts-based curriculum encounter occurred in a graduate course on arts-based research methods. For a class project we engaged in an inquiry on the question: “What does it mean to live on this land?” which we explored through various arts-based activities. The question challenged us to think deeply about our relationship with and responsibilities to the land we occupy. The inquiry raised for us and, in various ways, implicated us in issues around geographical settings, historical contexts, colonization and nationhood, relations as/with Indigenous peoples, Indigenous ways of knowing, relations with the natural environment, exploitation of the land, the environmental crisis, and our own family histories and personal journeys. In this paper, we share the reflective writings of four inquiry participants interspersed with some images from our work together.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".