SDG16+ implementation in fragile and conflict-affected states: what do the data tell us six years into Agenda 2030?
Bibliographic record
Abstract
SDG16+ on peaceful societies, justice and strong institutions is often presented as a ‘strategic lever’ to enable the implementation of other SDGs, hence the ‘+’ often added to that goal. This is especially the case in fragile and conflict-affected states (FCAS) where violence and weak governance are seen as major constraints on development. Six years into Agenda 2030, this paper triangulates official reports such as Voluntary National Reviews, and third-party sources, to ascertain the implementation of SDG16+ in a broad sample of FCAS and in seven specific fragile countries. We observe varying levels of effort on implementation, yet an overall trend towards superficial compliance. Informed by institutional theory, we argue that such uneven implementation is not just a function of limited data or resources, but of the varied commitment of elites to enable or prevent the consolidation of peace, justice and effective institutions.
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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 | 0.039 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".