Against ontological capture: Drawing lessons from Amazonian Kichwa relationality
Bibliographic record
Abstract
Abstract This article offers an experiment in theorising within or across a ‘space’ of ontological disagreement – which, as numerous authors have contended, characterises much that is at stake in relations between states and Indigenous peoples in the Americas. Such ontological disagreements, I argue, contain radical potential for disrupting globally dominant and anthropocentric patterns of thinking and relating, and for generating alternatives. I substantiate this point with reference to the relational ontologies informing different Indigenous ways of analysing and practicing existence. Drawing on Amazonian Kichwa thinking and Anishinaabe accounts of treaties, I show how these relational ontologies recast the problem of how it is possible to relate with difference, in such a way as to fold an inter-human ‘international’ into a continuum of relations that include human-nonhuman ones. Distinct normative horizons emerge. I argue that non-Indigenous people can draw a range of provocations here concerning our constitution as selves and the political space in which we understand ourselves to possibly participate. I also claim, however, that this more transformative potential is predominantly squandered through processes of what I call ontological capture, which troublingly re-entrench dominant construals of reality and forestall a more radical questioning and re-patterning of accompanying lifeways.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".