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Record W3171273537 · doi:10.1080/08865655.2021.1924074

“Crossing the River by Feeling the Stones”: How Borders, Energy Development and Ongoing Experimentation Shape the Dynamic Transformation of Yunnan Province

2021· article· en· W3171273537 on OpenAlexaffvenue
Thomas Ptak, Victor Konrad

Bibliographic record

VenueJournal of Borderlands Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsTransformation (genetics)FeelingEnergy (signal processing)Economic geographyGeographyRegional sciencePolitical scienceEnvironmental planningPsychologyPhysicsSocial psychology

Abstract

fetched live from OpenAlex

This article details the dynamic transformation of Yunnan’s border regime during the early twenty-first century through the Great Western Development Strategy, Bridgehead and Belt and Road Initiative. Although China’s macro-scale national strategies frame border change, experimental ideology and local-scale mechanisms drive border change and refine through constant adjustment. As illustrated in this article, border positioning and repositioning is incremental, constituted of many small and different components, dependent on locals and localities, and employing an array of border mediation strategies. Seemingly large scale border alteration is actually accrued through micro-changes, small scale experiments in specific places, trial and error adjustments and, essentially “crossing the river by feeling the stones.” Whereas Yunnan’s geostrategic location and vast energy resources have catapulted the province through rapid growth and into a globalized international context, ongoing transformation has also bolstered boundaries and bordering processes in Yunnan’s mobile border regime and generated antithetical and reactionary bordering responses that need to be viewed within a post-globalization border framework.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.016
GPT teacher head0.305
Teacher spread0.289 · 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 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

Citations12
Published2021
Admission routes2
Has abstractyes

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