Nudging to glory: the World Heritage Convention’s influence in conflict-prone Global South natural sites
Bibliographic record
Abstract
The paper explores the role of the UNESCO World Heritage Convention in safeguarding natural and cultural heritage, with a specific focus on sites facing armed conflict. The Convention acts as a global mechanism for the protection and conservation of sites with Outstanding Universal Value. The study investigates the use of ‘soft power’ and ‘nudging’ strategies by the World Heritage Committee to facilitate the restoration of World Heritage Sites facing threats, particularly in the Global South. The analysis is based on the examination of 32 natural sites inscribed on the List of World Heritage in Danger since 1984, nine of which are in the Global South and faced with armed conflict. Case studies illustrate how armed conflicts impact biodiversity and the steps taken to recover these sites. The study emphasises the soft power of the World Heritage Convention, backed by diplomatic ties and financial aid, as instrumental in achieving restoration. Nudging is observed in the strategic alignment of choices to encourage conservation efforts. The findings suggest that the World Heritage Committee’s influence extends beyond conservation, contributing to regional development, especially in the Global South. However, challenges persist, and the paper calls for a continuous evolution of the World Heritage Convention’s role in addressing conflicts, development, and climate change to ensure effective global heritage conservation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".