Ontology and indigeneity: on the political ontology of heterogeneous assemblages
Bibliographic record
Abstract
The first challenge faced by a project that seeks to bring concerns with ontology and indigeneity into a conversation is to sort out the various (and possibly divergent) projects that are being mobilized when the former term is used, not the least because what do we mean by ontology impinges upon how we can conceive indigeneity. In this article I play a counterpoint between two ‘ontological’ projects: one in geography, that foregrounds a reality conceived as an always-emergent assemblage of human and non-humans and troubles the politics that such assemblages imply. The other in ethnographic theory, that foregrounds that we are not only dealing with a shifting ontology, a (re)animated world, but also with multiple ontologies, a multiplicity of worlds animated in different ways. Thus, if the heterogeneity of always emerging assemblages troubles the political, the very heterogeneity of these heterogeneous assemblages troubles it even more. What kinds of politics and what kinds of knowledges does this troubling demand? I advance the notion of political ontology as a possible venue to explore this question.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.131 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 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".