Bibliographic record
Abstract
ABSTRACT Growth and degrowth have become key issues in the discourse on sustainability, and their quantification (growth/degrowth rates) has become integral to the limited number of indicators used to evaluate the ecological aspects of political and organizational decisions. This paper empirically examines the relevance of corporate growth and degrowth indicators for investors whose investment strategies align with the objectives of the United Nations Framework Convention on Climate Change (UNFCCC). Based on financial and environmental data from companies listed in the SBF 120 (the French large‐cap index) from 2016 to 2022, our findings indicate that investors' valuations of environmentally focused (green) firms are positively correlated with financial performance but appear insensitive to fluctuations in these firms' operational growth rates. Through integrating analyses of both green growth and degrowth enterprises, this study contributes to the debate by highlighting how the prevalent binary opposition between these concepts may hinder effective decision making and action. Also, our results prompt us to question the widespread prioritization of economic growth in public discourse. We thus propose adopting an “a‐growth” perspective as an alternative approach to more effectively advance environmental objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".