Movement, movie-making, and soil art: hope in affectively de/composing pedagogies
Bibliographic record
Abstract
In the complexities of current realities, we are a writing collective of educators interested in placing hope in the present, particularly in composing practices and pedagogies. Drawing on a post-qualitative methodology, we weave together examples of practices of dance/movement, digital movie-making, and soil artivism with specific concepts of affect-as/is-hope. As a counternarrative to writing pedagogies and assessments that are increasingly standardized, product-oriented, technical, rational, and disembodied, we introduce the term de/composing pedagogies to conceptualize the potential in processes of becoming/making/meaning-making that are always something else/more/different and even less. De/composing pedagogies help us name, describe, experience, and examine – individually and collectively – what is moving us and our practices more intensely and deeply; what is making possible new ways of being/becoming/doing for us and for our co-learners (e.g. the teachers and students we work with); and what is expanding potential for us as writers/composers/educators in and across various situations and contexts. As (re)sources of hope, we close with pedagogical questions related to composing through/with elements of experimentation, embodiment, an emphasis on relation and (re)connection, ephemeral and material elements, (e)motion, and the emergence of meaning-making.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".