Bibliographic record
Abstract
Our compost experiment plays out in two short provocations that work toward a list of provisional sensibilities to guide our knowing-together with nonhuman others as non-Indigenous theorists on Indigenous lands. In the first provocation, we briefly retell the history of colonial expansion as a matter of waste-making, and reframe Canadian-colonial occupation as a project of recycling—reproducing only beings and knowings that are deemed useful to nation-building. Eventually, and with the help of Indigenous science and feminist science studies, we cultivate a performative understanding of compost as a mode of multispecies storying that provokes accountability. In the second provocation, as we wonder how to tell stories that are culpable to and for their own telling, we hang on to the idea that nonhumans might have their own stories, or at least storied lifeways that generatively contribute to keeping on together in a place. We trace out crucial crossings between multispecies ethnography and Indigenous sovereignty by demonstrating how compost as a mode of attunement to nonhuman stories can be done in both theory- and art-making. Finally, we offer a tentative list of compost intimations that might guide our thinking, reading, and writing with human and nonhuman others as we continue to tend to our kinship obligations on Indigenous lands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".