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Record W4393155988 · doi:10.1080/08865655.2024.2330058

The Socio-Spatiality of Energy Borderlands – Multidimensional Discursive Practices Regarding the Turów Coal Mine Conflict

2024· article· en· W4393155988 on OpenAlexvenueno aff
Kamil Bembnista, Ludger Gailing

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsEnergy (signal processing)CoalSociologyPolitical scienceGeographyArchaeologyStatisticsMathematics

Abstract

fetched live from OpenAlex

An important component regarding the climate and energy crisis and its implications is the upheaval in energy production and supply as a fundamental transition in favor of low-carbon energy.The regions of the German-Polish border area studied in this paper encounter different development paths of coal and renewable energy sources.One emblematic recent case in this context, is the legal dispute over the closure of the Turw coal mine.The authors investigate to what extent these energy spaces develop between European frameworks, nation-state policies as well as regional and local implementations.The analysis is based on regional discourses of German and Polish newspapers with the highest circulation in the border area.The combination of a discourse analysis and a multidimensional, space-theoretical approach overcomes a simplification of socio-spatial strategies and enables a differentiation of the borderlands.Additionally, the examination of the discursive practices provides insights on scaling and network activities as well as on strategies of placeprotection in socio-material energy spaces in the German-Polish borderland.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.052
GPT teacher head0.408
Teacher spread0.356 · 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 designNot applicable
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

Citations4
Published2024
Admission routes1
Has abstractyes

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