Bibliographic record
Abstract
This proposition explores the potential of a pedagogy of affect as an arts- based research approach to museum education at the university level. Such an approach is predicated on a continuous movement of situated stories as the heart of the learning encounter, generated relationally between object-body-space, or artwork- learner-museum. As a forum for deliberation, the purpose of this conversation is to consider how emotions, as the basis for teaching with caring and sensory awareness, bring vitality, aliveness, and feelings to the fore. This conversation explores affective epiphanies sourced from personal practical knowledge as an expression of arts- research-in-progress. By drawing on autoethnographic life writing, I explore an alternate approach to three museum collections that demonstrate how and why the aesthetic relation of stories operate as pedagogic pivots in ways that reconfigure conventional museum engagement. Rethinking museum education with an arts research perspective is an effort to advance how context connects affective systems of knowing relationally, and why embracing stories offers new pathways to understand museum education through more expansive learning approaches, inclusive of feeling.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".