Supporting the Transition to Sustainability: SMART Reform Proposals
Bibliographic record
Abstract
Achieving sustainability is possible. The adoption of the UN Sustainable Development Goals (SDGs) in 2015 and the Paris Agreement in the same year created a new impetus for the debate on how to do so. With an emerging recognition of the serious risks of continuing with unsustainability, there is currently unprecedented support for change. This is reflected in the new EU Commission now towards the end of 2019, with its emphasis on an EU Green New Deal and a Just Transition. The transition to sustainability also has strong legal basis in the EU’s overarching goals set out in its Treaties, with duties to protect the environment, human rights and human dignity, within the EU and in the EU’s relations with the wider world. To achieve sustainability, we need to change the way business operates. The SMART project supports the transition to sustainability through a set of reform proposals aiming to change the way business and finance operate, and the way products are produced and consumed. We aim to make it possible and easy for business and finance to create value in a sustainable manner, and for products to be produced and consumed in a way that contributes to securing a safe and just space for humanity within planetary boundaries. As such, our reform proposals concern the EU as a global actor, and the EU as a legislator and policymaker. We invite comments and suggestions to our reform proposals, as presented both in this introductory report and in response to the detailed proposals that we will make available on our SMART website in the final months towards the project’s conclusion in February 2020.
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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 | 0.074 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.054 | 0.032 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".