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Record W2982219104 · doi:10.1111/amet.12839

Driving in terrain

2019· article· en· W2982219104 on OpenAlexfundno aff
Anna Kruglova

Bibliographic record

VenueAmerican Ethnologist · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersNational Research University Higher School of EconomicsUniversity of TorontoSocial Sciences and Humanities Research Council of CanadaWenner-Gren Foundation
KeywordsModernityPoliticsState (computer science)Political economyEurocentrismSociologyNegotiationGovernmentalityAutonomyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

ABSTRACT The developing culture of mass private automobile ownership in Russia became a prominent platform for post‐Soviet citizen‐drivers to (re)negotiate their relationship with the state. The convergence of power, infrastructure, and modernity in automobility made salient the old Soviet promise of infrastructural and cultural development, delegitimizing the post‐Soviet contraction of the state's sphere of responsibility. On the other hand, the inherent danger and autonomy of automobile technology, combined with highly spatialized local politics, reveal a number of political mechanisms and imaginaries that make such withdrawals peculiarly legitimate. Finally, through the windshield of a private car in Russia, the state emerges as the ontology and a total social fact. This contradicts the anti‐statist, pluralist, and the localizing concepts of the state in contemporary anthropology. [automobility, accidents, the state, modernity, politics of statelessness, Russia]

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0820.020

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 head0.021
GPT teacher head0.348
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2019
Admission routes1
Has abstractyes

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