Institutions as a Fundamental Cause for Long-Run Sustainability
Bibliographic record
Abstract
The development of sustainable societies relies upon the institutional structures that govern the relationship between economic systems and environmental leadership. In this study, we engage with the juxtaposition between inclusive and extractive institutions as potential molders of the trajectory of sustainability. Featuring governance indicators and economic metrics, we explore the impact of institutional inclusiveness and extractiveness on sustainability outcomes. Inclusiveness, which enables the prosperity of collective well-being through democratic governance, legal integrity, and innovation, is posited as an affirmative force for sustainability. In contrast, extractive institutions, which concentrate power and resources, hinder the realization of sustainable development by perpetuating inequality and exploitation. This paper suggests a statistic pipeline, comprised of econometric regression, machine learning models, and clustering analysis, to assess the impact of governance and economic performance on sustainability indices. We posit that inclusiveness serves as a potent driver for sustainability, particularly when coupled with economic resources, whereas extractiveness leads to the attenuation of sustainable progress. Through this work, we aim to gauge how institutions can either hinder or propel the material conditions necessary for the achievement of sustainability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".