Performance Auditing to Assess the Implementation of the Sustainable Development Goals (SDGs) in Indonesia
Bibliographic record
Abstract
Regular assessment of progress on the implementation of Sustainable Development Goals (SDGs) is crucial for achieving the goals by 2030 yet such assessments often require extensive resources and data. Here, we describe a method using performance auditing as a novel approach for assessing the implementation of SDGs that would be useful for countries with limited resources and data availability but might also provide an alternative to choosing particular goals and implementing them one at a time, for all countries. We argue that, instead of monitoring all 169 targets and 242 indicators, a country could assess the effectiveness of its governance arrangement as a way of ensuring that progress on implementing SDGs is on track, and hence improve the likelihood of achieving the SDGs by 2030. Indonesia is an archipelagic upper-middle-income country facing challenges in data availability and reliability, which limits accurate assessments of SDG implementation. We applied a standardized performance audit to assess the effectiveness of current governance arrangements for the implementation of SDGs. We used the Gephi 0.9.2 software (Open sourced program by The Gephi Concortium, Compiègne, France) to illustrate the regulatory coordination among public institutions. We found that Indonesia’s governance arrangements are not yet effective. They might be improved if Indonesia: (1) synchronize its SDG regulations; (2) redesigns its governance structure to be more fit for purpose; and (3) involves audit institutions in the SDG governance arrangements. These findings would likely apply to many other countries striving to implement the SDGs.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".