Bibliographic record
Abstract
As we are now past the half way mark of the UN Sustainable Development Goals (SDGs) for 2015-2030, it is a prudent time to take stock and look forward, and I do so here from my perspective as founding Editor in Chief of the <i>European Journal of Sustainable Development Research</i>. The SDGs clearly have fostered a much research and numerous initiatives and implementations related to sustainable development, spanning all sectors of societies and their economies. Progress made towards sustainable development has reinforced that there are multiple approaches to sustainable development, varying from region to region and country to country. Despite these advances and successes, progress to date on the SDGs has been far from adequate if humanity and society are to shift towards sustainable development in a significant and meaningful way in the future. This relatively weak progress has stemmed from various factors, some unpredictable and others somewhat foreseeable. It is becoming increasingly evident that the progress on the SDGs by 2030 will not complete the quest for sustainable development. I consequently believe and contend that there clearly is a need to extend and double down on the SDGs for 2030-2045 and beyond.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".