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Record W3155962850 · doi:10.1111/weng.12553

The linguistic landscape of Bukhara and Tashkent in the post‐Soviet era

2021· article· en· W3155962850 on OpenAlex

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.

Bibliographic record

VenueWorld Englishes · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUzbekIndependence (probability theory)PrestigePower (physics)Political scienceLinguistic landscapeCentral asiaGeorgianLinguisticsHistoryEconomic historyAncient history

Abstract

fetched live from OpenAlex

Abstract The sociopolitical changes that have taken place in Central Asia since the downfall of the Soviet Union have had a significant effect on the linguistic landscape of Uzbekistan. Russian, the dominant language of the Soviet era, lost its power and prestige in the wake of independence. Uzbek, the mother tongue of the local people, became the country's sole official language, while English, once considered the language of the Western bourgeoise, started to flourish as the most popular foreign language in post‐Soviet Uzbekistan. Using the concept of linguistic landscape, this study analyzes the presence and use of Uzbek, Russian, and English languages on public and private signs in two major Uzbek cities. The findings of the study reveal that even though it has been almost 30 years since Uzbek was declared as an official language, its presence on public and private signs is not as widespread as people might think.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.026
GPT teacher head0.371
Teacher spread0.345 · 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