Bridging the nature-culture divide: a biocultural reclassification of the World Heritage Sites
Bibliographic record
Abstract
The UNESCO World Heritage List comprises 1223 sites of outstanding universal value classified as natural, cultural, or mixed. However, only 40 sites (3%) are classified as mixed, highlighting a persistent challenge within the World Heritage Convention to recognize the interconnection between nature and culture. Furthermore, although 129 (11%) of sites are designated as Cultural Landscapes, exemplifying significant human-environment interactions, only 10 of these are mixed, reflecting a stronger emphasis on the cultural criteria. To address this nature-culture divide, we evaluated the explicit recognition of biocultural value within the World Heritage Sites. Using a recent definition of biocultural heritage, we leveraged large language models (LLMs) to reassess the classification of sites based on their official descriptions in the World Heritage List. Our findings reveal that up to 26% of the sites could be reclassified as biocultural, indicating that a substantial number of sites hold previously unrecognized biocultural value. Therefore, we advocate for a comprehensive biocultural reclassification of the World Heritage Sites, and suggest this effort could advance UNESCO’s vision toward a representative, balanced, and credible World Heritage List, which has not been achieved so far.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".