Comparative Analysis of Alignments between SDG16 and the Other Sustainable Development Goals
Bibliographic record
Abstract
SDG16 cuts through, affects and is affected by the other 16 SDGs. This study involved a number of steps; the first step was computing the degrees of compatibility/alignments of SDG16 targets as individual targets against the targets of the other 16 SDGs using a scoring system that ranged from -3 to +3. The next step in data treatment involved computing the means for each row in each table to get the compatibility scores between SDG16 targets and each goal and then we used the columns to compare the SDG16 with the targets of each of the other goals. The final steps involved computing the mean compatibility scores between the SDG16 goal and the goals of the other SDGs on one hand and those between the SDG16 targets and the other 16 SDGs on the other. The approach is based on the strategic management principle that objectives and targets are set in ways that their achievement help in the achievement of the aspirations of the goal. The study approach is new, and it has not been done before. The compatibility examination showed that the aspirations embedded in the development of SDG2 (Zero Hunger) targets and those of SDG16 were least aligned and do not effectively support each other. SDG16 and SDG5 (Gender equality) were the most aligned, suggesting that the achievement of the SDG16 targets go a long way in supporting the achievement of SDG5 targets. An approach like this can be used as stand alone or in conjunction with the often used qualitative methods and will be a very helpful tool during SDG or related reviews, as it is useful in identifying targets and goals with high mutual transfer benefits among themselves. The study concludes with some recommendations.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".