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Monitoring the Dynamics of Tensions in an Industrial Symbiosis: A Paradox Perspective

2023· article· en· W4385212854 on OpenAlexaffabout
François Labelle, Aliénor de Rouffignac, Pierre‐Olivier Lemire, Kadia Georges Aka

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsUniversité de MonctonUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPerspective (graphical)Dynamics (music)SymbiosisComputer scienceSociologyBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Industrial symbiosis is a means to achieve sustainability at the systemic level. It involves different organizations in mutual exchanges and symbiotic collaborations to develop sustainable innovative solutions. Little is known about the dynamics of the evolution and management of tensions in an industrial symbiosis. This study seeks to fill this gap by answering this question: How do tensions emerge, evolve, and be managed in an industrial symbiosis? Based on the paradox’s perspective, an analytical framework was built to monitor paradoxes in which various tensions coexist. It was applied to the case of an industrial symbiosis that aimed to establish the first forest residue biorefinery in Canada. The main results show that tensions are managed in four phases (tension identification, tension dynamics, paradox strategy, and paradox management). In particular, when a paradox leads to a tension of belonging (legitimacy and allegiance), a proactive “more-than” strategy (developing a new and creative synergy for transcending the paradox by a dynamic and interactive connection between the stakeholders) is needed under contextual factors to integratively manage the paradox and mitigate the tension. This study contributes to the sustainability management literature by suggesting a monitoring tool that can capture the dynamics of tensions and identify paradox strategies to manage them. This provides guidance for managers and other stakeholders involved in industrial ecology and symbiosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.290
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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 routes2
Has abstractyes

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