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Record W2936711264 · doi:10.1080/13562576.2019.1594752

Niches of agency: managing state-region relations through law in Russia

2019· article· en· W2936711264 on OpenAlex
Gail Fondahl, Viktoriya Filippova, Antonina Savvinova, Aytalina Ivanova, Florian Stammler, Gunhild Hoogensen Gjørv

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSpace and Polity · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsUniversity of Northern British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNorges ForskningsrådRussian Foundation for Basic Research
KeywordsAgency (philosophy)NegotiationLegislationState (computer science)PopulationPolitical sciencePower (physics)Government (linguistics)Federal lawLawPublic administrationSociology

Abstract

fetched live from OpenAlex

State-region relations involve negotiations over the power to (re)-constitute local spaces. While in federal states, power-sharing ostensibly gives regions a role over many space-making decisions, power asymmetries affect this role. Where centralization trends may erode regional agency, law can provide an important tool by which regions can assert influence. We examine a case where, in response to a proposed Russian federal law highly unpopular with a regional population, the region's government sought to ameliorate its potential impacts by using opportunities to co-produce the law, amending regional legislation, and strategically implementing other federal and regional laws to protect its territory.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.297
Teacher spread0.276 · 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