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Record W4412112310 · doi:10.1177/13505084251350847

Scaling sustainability in businesses with a post-growth orientation: An exploratory empirical study

2025· article· en· W4412112310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOrganization · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of ManitobaSaint Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityEmpirical researchScalingOrientation (vector space)Exploratory researchBusinessEconomic geographyIndustrial organizationEconomic systemEconomicsSociologyEpistemologySocial scienceMathematics

Abstract

fetched live from OpenAlex

To avoid negative social and ecological consequences associated with growth-based economics, businesses with a post-growth orientation intentionally forgo scaling for profit maximization and instead seek to scale positive socio-ecological impacts. This study investigates 35 small and medium-sized businesses (33 private and 2 cooperatives) with a post-growth orientation to examine how they strive to scale their positive socio-ecological impact. Our findings reveal four distinct scaling approaches: Enhancement, Expansion, Bridging, and Collaboration. We provide a typology of these four approaches and explain how they can be interlinked to create synergy. Our study shows how private businesses can participate in creating a post-growth economy, thus broadening the scope of previous post-growth research that has focused on other organizational forms such as social enterprises, benefit corporations, and cooperatives.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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