Multispecies collaboratories: reconfiguring children’s more-than-human entanglement with colonization, urban development and climate change
Bibliographic record
Abstract
This paper shows how multispecies collaboratories work to complexify understandings of shared space with more-than-human others through collective inquiry and experimentation. Recognizing tensions produced through engaging childhood geographies research on stolen land, it proposes multispecies collaboratories as inextricably situated within the uneven flows of colonization, urban sprawl, and climate change. The authors present six collaboratory sticking points – the politics of green, confronted by the unexpected, bearing witness, boundary bursting, root problems, and troubling entanglement – to highlight possibilities and incommensurabilities inherent within. Sharing moments on Anishnaabe, Attawandaron, Lūnaapéewak, and Haudenosaunee territories in London, Ontario, and lək̓ʷəŋən and WSÁNEĆ territories in Victoria, British Columbia, they take a common worlding approach to reconsidering young children’s multispecies relations as always and already political, multifarious world-making forces. They argue for multispecies collaboratories as small but significant sites for unhinging children’s geographies from neoliberal, colonial logics, asking: What kinds of experimentation make it possible to activate a collective sense of relationality and reciprocity with the myriad of creatures with whom children share space? What is required of us to do so without retooling the colonial logics that contribute to the erasure of Indigenous peoples, more-than-human kinships, and connections to place?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".