Bibliographic record
Abstract
The turn to ontology, often associated with the recent works of Philippe Descola, Eduardo Viveiros de Castro, and Bruno Latour, but evident in many other places as well, is, in Elizabeth Povinelli's formulation, “symptomatic” and “diagnostic” of something. It is, I here argue, a response to the sense that sociocultural anthropology, founded in the footsteps of a broad humanist “linguistic” turn, a field that takes social construction as the special kind of human reality that frames its inquiries, is not fully capable of grappling with the kinds of problems that are confronting us in the so-called Anthropocene—an epoch in which human and nonhuman kinds and futures have become so increasingly entangled that ethical and political problems can no longer be treated as exclusively human problems. Attending to these issues requires new conceptual tools, something that a nonreductionistic, ethnographically inspired, ontological anthropology may be in a privileged position to provide.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".