Classifying Sustainable Development Goal trajectories: A country-level methodology for identifying which issues and people are getting left behind
Bibliographic record
Abstract
How useful are the Sustainable Development Goals for conducting empirical analysis at the country level? We develop a methodological framework for answering this question, with special emphasis on the SDGs' normative ambition of "no one left behind." We first classify all 169 SDG targets and find that 78 incorporate an outcome-focus that is quantitatively assessable at the country level, including 43 through a systematic approach to establishing "proxy targets." We then present a framework for diagnosing the embedded diversity of absolute and relative indicator trajectories in a harmonized manner, based on a country's share of its starting gap on course to be closed by the relevant deadline. In turn, we present a method for estimating the human consequences of falling short on targets, measured by the number of lives at stake and people's basic needs at stake. As a case study, we apply the framework to Canada, an economy not commonly examined in the context of global goals. We are able to assess a total of 61 targets through the use of 70 indicators, including 28 indicators drawn from the United Nations' official database. Overall, we find Canada is on course to succeed on 18 indicators; to cover at least half but less than the full objective on 7 indicators; to cover less than half the required distance on 33 indicators; and to remain stagnant or move backwards on 12 indicators. Among indicators assessed, the country is only fully on track to achieve one SDG. Shortfalls suggest approximately 54,000 Canadian lives at stake and millions of people left behind on issues like poverty, education, intimate partner violence, and access to water and sanitation. Our diagnostic framework enables considerable, if only partial, quantification of a country's SDG challenges, recognizing the wide range of contexts for underlying data availability and societal problems.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".