MétaCan
Menu
Back to cohort
Record W4406641222 · doi:10.1016/s2542-5196(24)00310-3

Post-growth: the science of wellbeing within planetary boundaries

2025· review· en· W4406641222 on OpenAlexaff
Giorgos Kallis, Jason Hickel, Daniel W. O’Neill, Tim Jackson, Peter A. Victor, Kate Raworth, Juliet B. Schor, J. Steinberger, Diána Ürge-Vorsatz

Bibliographic record

VenueThe Lancet Planetary Health · 2025
Typereview
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsYork University
FundersEconomic and Social Research Council
KeywordsPlanetary boundariesAstrobiologyBiologyEcologySustainability

Abstract

fetched live from OpenAlex

There are increasing concerns that continued economic growth in high-income countries might not be environmentally sustainable, socially beneficial, or economically achievable. In this Review, we explore the rapidly advancing field of post-growth research, which has evolved in response to these concerns. The central idea of post-growth is to replace the goal of increasing GDP with the goal of improving human wellbeing within planetary boundaries. Key advances discussed in this Review include: the development of ecological macroeconomic models that test policies for managing without growth; understanding and reducing the growth dependencies that tie social welfare to increasing GDP in the current economy; and characterising the policies and provisioning systems that would allow resource use to be reduced while improving human wellbeing. Despite recent advances in post-growth research, important questions remain, such as the politics of transition, and transformations in the relationship between the Global North and the Global South.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.341
Teacher spread0.301 · 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
GenreReview

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

Citations174
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Planetary HealthSame topicGlobal Energy and Sustainability ResearchFrench-language works237,207