TRANSITION TO SUSTAINABLE ECONOMY - REVIEW OF POLICY CHOICES SUGGESTED BY INTERNATIONAL AGENCIES
Bibliographic record
Abstract
Globally, there have been significant efforts to develop new means of assessing progress beyond GDP in recent years. The recent publication by the UN Secretary-General of a policy brief (UN, 2023) is inviting member states to move beyond GDP by measuring what truly matters for sustainability and prosperity. The policy brief outlines the shortcomings of GDP as an indicator of summarizing everything too much and revealing too little to be able to adequately inform policy (UN, 2023; pp. 12). This paper will present a review of a number of recent documents (2019 � 2022) published by international agencies to explore their views on the role and types of growth to achieve their objectives such as reducing emission, improving resilience, protecting biodiversity and so on. The paper will discuss two approaches to growth and development. Most of the listed reviewed documents suggest approaches for governments to move towards sustainable, inclusive, and sustained economic growth. Second set of recommended approaches focuses on the importance of broadening the indicators used to describe economic growth, often referred to as �moving beyond GDP�. The paper will provide details on each of the suggested approaches and potential linkages and win-win solutions to improve pressing global challenges.
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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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".