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Introduction: The “Ontological Turn” in Russian Anthropology: Turning towards Materiality, Nonhuman Agency, and Hybridity

2022· article· en· W4206500892 on OpenAlexvenueno aff
Sergey Sokolovskiy

Bibliographic record

VenueAnthropologica · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsMateriality (auditing)AnthropologySociocultural anthropologyHybriditySociologyOntologyApplied anthropologyFour field approachDigital anthropologyEthnographySocial anthropologyMedical anthropologyCultural anthropologyAnthropology of artEpistemologyHuman scienceAgency (philosophy)Social scienceHistoryPhilosophyAestheticsArt history

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0280.002
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.000

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.

Opus teacher head0.055
GPT teacher head0.395
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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