The Nexus between Cultural Heritage and the Sustainable Development Goals: A Network Perspective
Bibliographic record
Abstract
The sustainable development goals (SDGs) were adopted by UN in 2015 as a framework to guide collective efforts towards sustainability globally. More recently, it has been argued that we also need to explore sustainable development from a more localized lens since regions and cities face their own specific challenges. It is also necessary to obtain comprehensive empirical evidence of local business networks collectively contribute to the SDGs since the very last goal of the SDGs is about partnership. We engage these topics by analyzing the nexus between cultural heritage and sustainability through local cultural and creative industries. By analyzing business investment networks around 1355 cultural heritage sites composed of 66473 organizations in 293 cities in China, we found evidence that a city’s investment network structure and composition matter for a city’s progress in SDGs. Specifically, the network structure of many cohesive communities contributes to progress in local SDGs, and more frequent local knowledge sharing (compared to trans-city knowledge sharing) strengthens this relationship. As one of the first studies exploring the nexus between cultural heritages and the SDGs of cities through network analysis, it provides new insights on importance of integration of local actors in achieving 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".