Living protocols: remaking worlds in the face of extinction
Bibliographic record
Abstract
We are members of the Creatures Collective, a transnational group of Indigenous and non-Indigenous scholars, activists, artists, and communities who are collaborating to challenge the world-breaking violence of extinction by directly and collaboratively fostering alternatives to the dominant biodiversity-conservation paradigm. In this collection, we ask how, as co-researchers with(in) Indigenous communities, we can contribute to the remaking of relationships that foster more-than-human accountability, reciprocity, and capacities for resistance. We call these relationships living protocols – living not just in the sense that they are vitally alive, responsive, and regenerative, but also in the sense that we aim to actively live them by supporting those who enact and (re)make them. Based on collaborative research in so-called Australia, Canada, Malaysia, the Philippines, and the US, our essays seek to manifest research that is (or aims to be) collaborative, embedded in mutualistic, interpersonal, more-than-human relationships, and thus co-constitutive of the worlds those relationships sustain. By bringing these collaborations into dialogue with feminist, neomaterialist, decolonial, and Indigenous geographies, we aim to provoke discussion of how researchers across disciplines might contribute to the remaking of worlds in which plural life forms can co-exist – even in the face of transversal world-breaking.
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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 | 0.052 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.092 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".