Bibliographic record
Abstract
In 2015, all 193 member countries of the United Nations adopted the 2030 Agenda for Sustainable Development. It includes 17 Sustainable Development Goals (SDGs). Building on the principle of “leaving no one behind,” it emphasizes a holistic approach to achieving sustainable development [1]. The 2020 environmental, social and governance (ESG) scoring and reporting document from the Organization for Economic Co-operation and Development (OECD) notes that sustainability investing has grown, primarily due to the number of funds and investors that have added ESG approaches to their overall agenda. Corporations, central banks and the public sector are placing a new emphasis on a greener environment and low-carbon economy [2]. The 2020s was to be a decade of action but progress has been slow, stalled or reversed in meeting the 17 SDG targets [3]. OECD’s quantitative analysis provides an indication of the progress made and challenges still ahead with regard to sustainable development. The wide variety of metrics, methodologies, and approaches indicate a high number of disparate outcomes that are open to interpretation [4].
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".