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 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+.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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; both teacher heads agree on what is shown here.
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".