Bibliographic record
Abstract
Designed as a follow-up to the Millennium Development Goals (MDGs), which guided international development policies from 2000 to 2015, the 2030 Agenda proposed a new development road map for the subsequent fifteen-year period. The most iconic SDGs deal with the eradication of poverty and hunger, the fight against climate change, and the creation of a global partnership for sustainable development. The chapter shows that the SDGs’ script was not written in advance for the UN supertanker does not follow a predetermined route. The 2030 Agenda is also a useful reminder that for every global public policy adopted, alternative courses of action that were once part of the conversation are discarded along the way. Our analysis illuminates not only the experimental nature of the SDGs’ creation but also the power relations and the political choices that the SDGs reflected. Among other things, the 2030 Agenda was also profoundly marked by a set of practices related to goal-setting. In addition, convergence around sustainable development can be seen as the silver bullet of the 2030 Agenda, together with the idea that global poverty must be eradicated and that in this process, no one should be left behind.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".