Queer Synthetic Curriculum for the Chthulucene: Common Worlding Waste Pedagogies
Bibliographic record
Abstract
This paper sketches aspects of common worlding waste pedagogies through Donna Haraway’s figure of the Chthulucene. More specifically, it narrates the making and happenings of what we call a queer synthetic curriculum in an early childhood centre. Drawing attention to plastic in order to reframe children’s relationship to it, the article engages with three main questions: How might we refashion waste practices from children’s ubiquitous plastic relations? How might we speculate on the kinds of response-able worlds that might be remade through new kinds of interactions between child and plastic bodies? What might the Chthulucene synthetic futures of early education entail? The queer synthetic curriculum also experiments with creative strategies to learn to live with plastic toxicities without necessarily celebrating them; it embraces the mixed affects that plastic affords (its sensorial pleasures and possibilities as well as the guilt embedded in their toxicity); it plays with the provocative idea that we can no longer separate our fleshy human bodies from synthetic polymer bodies; and it treats plastic as chthonic queer matter. We argue that, by staying with the trouble these risky attachments bring, conditions for futures other than those already determined by synthetic, toxic petrocapitalist modernity and coloniality might emerge in early childhood education.
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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 | 0.002 | 0.002 |
| 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 | 0.017 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".