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Record W4408373780 · doi:10.1080/14649357.2025.2472618

Reassembling Collaborative Planning for Socio-Technical Transitions: An Actor-Network Theory Approach

2025· article· en· W4408373780 on OpenAlexaff
Miguel L. Navarro-Ligero, Julio A. Soria-Lara

Bibliographic record

VenuePlanning Theory & Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsTransport Canada
FundersAgencia Estatal de Investigación
KeywordsActor–network theorySociotechnical systemSociologyKnowledge managementComputer scienceProcess managementPublic relationsBusinessPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Planning networks are the dominant instrument for collaborative planning scholars to describe distributed modes of social organization. This limited perspective cannot capture the diversity of collaboration forms that emerge in sociotechnical transitions. The paper proposes the actor-network concept as an alternative to assess which new forms of collaboration align planning with socio-technical transitions. Inspired by Actor-Network Theory (ANT), it traces controversies related to the model of implementing Low Emission Zones (LEZs) in Spain and elaborates a narrative showing how traditional planning actors reassembled into unexpected associations.

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)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0040.018
Scholarly communication0.0070.015
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.049
GPT teacher head0.426
Teacher spread0.377 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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