Walking-wit(h)nessing: propositions for walking with waste landscapes in early childhood education
Bibliographic record
Abstract
This paper takes walking-based research in early childhood education as a propositional space, one grounded in reconsidering witnessing as a practice attuned to co-emergence in waste landscapes. I draw from Ettinger’s (2001) and Boscacci’s (2018) word-concept wit(h)nessing to stake out some possibilities for walking-wit(h)nessing as an affective, relational practice for pedagogical responses to child-waste subjectivities in the ongoing global waste crisis. In doing so, I am careful to frame walking alongside waste landscapes as an invitational move towards resisting passive observation, one that refuses to extricate children, educators, and researchers from living-and-becoming with waste. I conclude by offering three propositions for walking-wit(h)nessing waste landscapes that open towards walking practices that embrace the tensions of waste and human/more-than-human enmeshment for enacting pedagogies that confront and counter status quo waste logics of invisibility, the problematics of scale, and solvability.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".