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Record W4403077563 · doi:10.14207/ejsd.2024.v13n3p212

Sustainable Development Revisited: Rethinking Growth-Centric Paradigms Through the Lens of the Sabbath

2024· article· en· W4403077563 on OpenAlexaff
Molly Graham

Bibliographic record

VenueEuropean Journal of Sustainable Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLens (geology)Sustainable developmentPolitical scienceOpticsPhysicsLaw

Abstract

fetched live from OpenAlex

This paper argues that the current growth-centric paradigm of sustainable development is fundamentally flawed and proposes a paradigm shift towards the biblical concept of the Sabbath. It critiques the internal contradictions of the SDGs and challenges the unsustainable pursuit of infinite growth in addressing social and ecological crises. By exploring Sabbath's principles of rest, recalibration, and redistribution, the paper offers an ethical foundation for self-limitation and a basis for human identity beyond productivity—addressing the shortcomings of the degrowth movement. It proposes practical applications of Sabbath principles at individual, community, and structural levels, drawing upon empirical examples to demonstrate their potential as pathways to environmental justice. These multi-level applications aim to bridge the gap between the intentions of international agreements and impactful local action. The paper concludes that Sabbath presents a transformative vision for sustainable development and environmental justice, fostering a harmonious relationship between humanity and the planet. Keywords: Sustainability, Development, Sabbath, Degrowth, Capitalism, Environmental Justice, Poverty, Well-being, Ethics, Religion.

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)0.010
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.078
Scholarly communication0.0090.011
Open science0.0020.012
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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
Published2024
Admission routes1
Has abstractyes

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