Bibliographic record
Abstract
It is acknowledged by International declarations and policy guidance documents that cultural heritage (CH) can contribute directly to many of the Sustainable Development Goals (SDGs), including resilience and adaptation to climate change (SDG 13). CH can support climate change action as it conveys local knowledge that builds resilience for change through mitigation and adaptation. Moreover, the vulnerability of the built environment to climate change possesses inherent resilient properties that allow it to resist damage. The integration of policies and practices of CH conservation into the wider framework of sustainable urban development entails the application of a landscape approach that (i) responds to local cultural contexts and value systems, (ii) integrates distinct theoretical perspectives to address the complex layering of the spatial, mental, and functional process-related dimensions of the landscape, and (iii) addresses policies and governance concerns at international and local levels (Ginzarly et al., 2019). Yet, the application of a landscape approach to CH conservation in the context of climate change is faced with different challenges.First, while at the turn of the twenty-first century the concept of CH has extended from monuments to cultural landscapes and cities as living heritage, assessment processes have been slow to evolve and address the interdisciplinary nature of heritage (Déom & Valois, 2020). Second, there is a challenge around assessing the vulnerability of CH to climate change and integrating its vulnerability status into the broader context of sustainable urban development. This challenge is imposed by the lack of a framework that addresses landscapes rather than heritage sites in isolation (Cook et al., 2021).To address the above-mentioned challenges, this presentation presents a landscape people-centered conceptual framework for resilient CH that is applicable at the city scale (i) to map how different stakeholder groups value heritage in the context of climate change, (ii) using social networks as a tool to engage communities and get access to information about heritage values, and (iii) assess the vulnerability of urban heritage and its associated values to climate change.The conceptual framework is structured around four prominent themes: (1) the city is a living heritage that encompasses the physical, mental, and digital heritage landscapes; (2) digitally mediated heritage practices provide new prospects for digitally-enabled forms of co-creation of heritage values; (3) longitudinal records on social media serve as a data source for the assessment of heritage values and their vulnerability to change; and (4) online communities contribute to communities’ disaster resilience.ReferencesCook, I., Johnston, R., & Selby, K. (2021). Climate Change and Cultural Heritage: A Landscape Vulnerability Framework. The Journal of Island and Coastal Archaeology, 16(2–4), 553–571.Déom, C., & Valois, N. (2020). Whose heritage? Determining values of modern public spaces in Canada. Journal of Cultural Heritage Management and Sustainable Development, 10(2), 189–206.Ginzarly, M., Houbart, C., & Teller, J. (2019). The Historic Urban Landscape approach to urban management: A systematic review. International Journal of Heritage Studies, 25(10), 999–1019.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".