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Record W4225388755 · doi:10.1177/10860266221092167

Metaphorical Mapping for Sensemaking and Sensebreaking of Stakeholder Relations in Sustainability Frames

2022· article· en· W4225388755 on OpenAlexaff
Kalyani Menon

Bibliographic record

VenueOrganization & Environment · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsConceptualizationSensemakingCorporate sustainabilitySustainabilityStakeholderFrame (networking)Frame analysisCorporate social responsibilitySociologyPsychologyCognitive reframingPolitical scienceSocial psychologyPublic relationsEngineeringLinguisticsPhilosophyMechanical engineeringEcology

Abstract

fetched live from OpenAlex

This article develops a framework for managerial conceptualization of corporate sustainability–stakeholder relationships (CS-SR) for paradoxical frames. The embedded nature of the business case frame for sustainability and aligned CS-SR, and a lack of insight into CS-SR for a paradoxical frame, may impede implementing a paradoxical frame for sustainability. Therefore, this article offers an understanding of structural differences in CS-SR in a business case versus a paradoxical frame for sustainability in terms of agency and communion. It then presents conceptual metaphorical mapping as the cognitive mechanism for managerial conceptualization of CS-SR for a paradoxical frame. Identifying nurturant parenting as an apt metaphorical domain with a conceptually similar relational structure to CS-SR of the paradoxical frame and dissimilar from the business case frame, it presents a model where juxtaposing nurturant parenting with sustainability enables sensebreaking of CS-SR of the business case frame and sensemaking of CS-SR of the paradoxical frame.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.022
Scholarly communication0.0050.012
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.193
Teacher spread0.175 · 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 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

Citations11
Published2022
Admission routes1
Has abstractyes

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