Leave no one behind: Prioritising equality and equity towards integration of global sustainability governance
Bibliographic record
Abstract
Processes within global sustainability governance are fragmented despite calls for greater synergies. Although “leave no one behind” is a universal value of the 2030 Agenda for Sustainable Development, evidence of concrete efforts to decrease inequalities, especially between countries in the Global South and North, are limited. In order to understand the impacts of fragmented global sustainability governance on inequality reduction and equitable development, I conducted text analysis of the 2030 Agenda's Sustainable Development Goals, the Paris Agreement on climate change, and the Sendai Framework for Disaster Risk Reduction, and reviewed literature on the convergences and gaps among them. I found that first, although the three global agreements acknowledge the need to realise equality and equity, the use of these terms are sparing in the texts, and when they do appear, they are defined inconsistently and operationalized imprecisely across these global processes. Second, while numerous linkages exist among the three agreements, sustainability challenges that directly relate to equality and equity outcomes are the ones with the least convergences identified, and have the least number of activities implemented, research done, and data collected. Such fragmented and inconsistent implementation and monitoring of progress towards sustainable development are detrimental to equitable development, and negatively affect marginalised groups—especially in the Global South—for whom impacts of disasters, climate change, and maldevelopment are felt most acutely. At more than halfway in the implementation of these global processes, it is important now more than ever to strengthen efforts towards their integration by prioritising equality and equity.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".