Bibliographic record
Abstract
Agenda 2030 has generated enthusiasm but also intense debates about its motivations, prospects, and limitations. Sustainable Development Goal (SDG) optimists at the UN see the goals and machinery of implementation as an historic opportunity to align global-to-local partnerships behind an integrated, transformative, and sustainable development effort to ‘leave no one behind.’ Pessimists like Weber (2017) see the SDGs as a universal project to designed to align policies on a technocratic, neo-liberal project; they worry that it could demobilize activists advocating for truly transformative alternatives. This paper explores a potential bridge between those views. It draws on Cox&s;s neo-Gramscian critique of international organizations (1983), as well as on nuanced analyses of Agenda 2030 ( Fukuda-Parr and McNeil, 2019 ; Dalby, Horton and Mahon, 2019) to revisit the ambiguous character of the SDG project. It delves into the politics shaping uneven SDG outcomes by examining the tough test of (dis)ability inclusion – codified especially in goals 1, 3, 4, 5, 8, 10, and 16. Those issues are explored at the global level and in two most different cases: Canada, as a member of the OECD; and Haiti, as a fragile and conflict-affected member of the g7+.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".