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Record W4323046551 · doi:10.1017/mor.2022.47

Relational Distance and Transformative Skills in Fields: Wind Energy Generation in Germany and Japan

2023· article· en· W4323046551 on OpenAlexaff
Manuel Nicklich, Takahiro Endo, Jörg Sydow

Bibliographic record

VenueManagement and Organization Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Victoria
FundersH2020 Marie Skłodowska-Curie ActionsHans Böckler Stiftung
KeywordsTransformative learningGermanField (mathematics)Organizational fieldDiversity (politics)SociologyWind powerPolitical sciencePublic relationsSocial scienceEngineeringPedagogyMathematicsGeographyInstitutional theoryLaw

Abstract

fetched live from OpenAlex

ABSTRACT Organizational interactions in fields, including their antecedents and consequences, remain under-researched, in particular with regard to relational distance and transformative skills. Through a comparative study of the German and Japanese wind power sectors, we explore the importance of distance among organizational actors and the development of skills. While in the case of Germany a radical increase in wind energy generation can be witnessed, the situation in the field of Japanese wind power remains largely unchanged. We show how different degrees of distance among organizational actors in these two countries result in the different development of skills that stimulate transformation in the field of energy generation. More precisely, we illustrate the pivotal role of distant challengers with their transformative skills for the successful conversion of already established field structures. Our study contributes to field theory by elaborating on the understanding of the evolution of relational distance, thereby grasping the dynamic interplay between the diversity of actors and their skill formation within a certain strategic action field.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.205
Teacher spread0.192 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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