Understanding policy integration of the Sustainable Development Goals: A Network Theory approach
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) have been much debated but there is no question that their goal is to achieve a more balanced and sustainable planet. This study systematically reviewed literature in order to determine the key criteria needed to implement such goals. A search of Scopus indexed journals as well as grey literature published up to March 2020 yielded 114 papers for examination. Using a Network Theory approach, the study found 14 common criteria for successful policy integration of the SDGs. The findings from this paper support that the achievement of the SDGs needs a comprehensive and holistic approach to achieve them. Through an assessment of many different fields of study, commonalities or networks occurred demonstrating how one goal can support and be a means to an end to achieve another. Conclusions consider that using Network Theory to assess tourism policies may assist in the success of policies for the achievement of the SDGs due to its interrelated nature.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".