Speculative caring collaboratories: mattering research alternatives
Bibliographic record
Abstract
This paper explores how ‘caring collaboratories’ advance new possibilities for imagining and enlivening early childhood education and research. Thinking with María Puig de la Bellacasa’s (2017) feminist materialist theory of care the authors bring a retrospective analysis to their research experiences within three different climate change focused collaboratories. The paper begins with an overview of their research sites, anlaysis method for this paper, and common worlds orientation. This is followed by a brief introduction to Puig de la Bellacasa’s conceptualization of care and an offering of three research stories to examine what carrying her triptych of care into the collaboratory proposes to researchers and educators committed to human and more-than-human living well together. The paper concludes with speculative considerations for what such caring collaboratories might offer childhood studies with/in the conditions of our times.
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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 | 0.074 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.104 |
| Scholarly communication | 0.022 | 0.047 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".