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Record W3053091772 · doi:10.1177/0007650320949843

A Time and Place for Sustainability: A Spatiotemporal Perspective on Organizational Sustainability Frame Development

2020· article· en· W3053091772 on OpenAlexafffund
Daina Mazutis, Natalie Slawinski, Guido Palazzo

Bibliographic record

VenueBusiness & Society · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityTypologyPerspective (graphical)Sense of placeFrame (networking)SociologySustainability organizationsValue (mathematics)GlobalizationSustainable developmentKnowledge managementEpistemologyBusinessPolitical scienceComputer scienceSocial scienceArtificial intelligenceEcology

Abstract

fetched live from OpenAlex

In this article, we explore how sense of time and sense of place shape the development of organizational sustainability frames (OSFs). Time and place are fundamental cultural assumptions that influence the way organizations form these frames. Given that globalization and digitalization have fundamentally altered how organizations experience and value time and place, we develop a typology of OSF development and theorize how an organization’s sense of time and sense of place interact to shape the content and structure of OSFs. In so doing, we offer a spatiotemporal perspective to the sustainability literature by bringing together previously disconnected research streams on time and place. We also highlight the importance of understanding the origins, not just the outcomes, of OSFs.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0050.025
Scholarly communication0.0090.015
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.220
Teacher spread0.212 · 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 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

Citations43
Published2020
Admission routes2
Has abstractyes

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