Wit(h)nessing Blasted Landscapes: Waste Pedagogies in Early Childhood Education
Bibliographic record
Abstract
The effects of the global waste crisis are actively reshaping the common worlds of early childhood, resulting in ecological inheritances of what Anna Lowenhaupt Tsing has named blasted landscapes. While blastedness has many shapes, childhood and waste intersect via one notable form, where post-closure and decommissioned landfills are increasingly converted into public recreation sites. Waste and discard studies scholars have drawn attention to the scalar discrepancies between industrial and post-consumer waste, yet environmental education frequently reinforces separation from and individual responsibility for waste, eliding the multi-scalar incongruence between the origins and the scope of the crisis. This dissertation traces a four-month inquiry with early childhood educators and children to generate conceptual and pedagogical orientations for reimagining child-waste relations at the Glenridge Quarry Naturalization Site, a former landfill, now recreation site in southern Ontario. Drawing on Louise Boscacci’s word-concept wit(h)nessing and inviting interdisciplinary perspectives to help frame the encounter-exchange it makes visible, this walking-based post-qualitative inquiry insists on proximity and takes waste as a collective co-constitutive presence for human and more-than-human common worlding. Across four articles, this dissertation introduces wit(h)ness marks as a conceptual and pedagogical orientation, traces children’s multispecies encounters with life and death in waste landscapes, offers propositions for walking-wit(h)nessing, and lastly, activates seed-bombing in waste landscapes as recuperative pedagogies for early childhood education. The inquiry offers important contributions for taking up waste relations as a pedagogical concern for early childhood education, where educators and children must be prepared to meet the world as it is, while working pedagogically toward more just shared waste futures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".