Introduction: The “Ontological Turn” in Russian Anthropology: Turning towards Materiality, Nonhuman Agency, and Hybridity
Bibliographic record
Abstract
The authors in this thematic issue reflect on the current “ontological turn” in Russian social sciences and humanities, and especially on the influence the turn exerts on various anthropological sub-disciplines and research domains. This introduction reviews publications in Russian academic journals, article collections, theses, books, and book chapters that best illustrate current ontological preoccupations in Russian anthropology. The ontological turn encompasses diverse interests and topics and is often labelled as “material,” “object-oriented,” “speculative-realist,” or “praxiographic.” In fact, we are dealing with multiple interdisciplinary “turns” that intersect and overlap, while interlinking many domains of the biological sciences, geographical sciences, social sciences, and humanities. In Russia, the ontological turn (actor-network theory, material semiotics, symmetrical anthropology, sociology of translation, object-oriented ontology, speculative realism) unfolds in different domains of research that can be grouped into four main fields: 1) medical anthropology, body studies, and death studies; 2) urban anthropology; 3) anthropology of science and techno-anthropology; 4) museum anthropology and material culture studies. The contributions to this issue illustrate current research in medical anthropology, body and death studies, urban anthropology, technoanthropology, museum studies, as well as Siberian ethnography using the perspectivist model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".