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Organizing in the Era of the Anthropocene: The Role of Human Agency in Building Resilient SESs

2022· article· en· W4286620672 on OpenAlexaff
M. Saidur Rahman, Monika Winn, Stefano Pogutz

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsAnthropoceneAgency (philosophy)Scope (computer science)Human systems engineeringResilience (materials science)Environmental ethicsSustainabilityPsychological resilienceEnvironmental resource managementConceptual frameworkEarth system scienceSociologyPolitical scienceEnvironmental planningGeographyEcologySocial scienceComputer scienceEnvironmental sciencePsychology

Abstract

fetched live from OpenAlex

Naming our current geological epoch the Anthropocene reflects human impact on social, physical and ecological systems as unprecedented in scale and scope. It also highlights the critical role of human agency and human organizing for reversing current trends. Aiming to contribute to research on this important topic, this paper builds on work from the natural sciences and conceptualizes organizations as part of interconnected social-ecological systems (SESs). To lay theoretical foundations for future research, we develop a comprehensive conceptual framework of first- and second-order system properties, illustrated by the case of the superweeds epidemic transforming agriculture globally. We advance organizational sustainability research by highlighting the critical, yet understudied, property of human agency to a system’s resilience and adaptive capacity.

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)0.005
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.021
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.376
Teacher spread0.302 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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