Takht-e-Soleyman and Head-Smashed-In Buffalo Jump: the recognition and conservation of world heritage cultural landscapes
Bibliographic record
Abstract
This research emphasizes that cultural landscapes are places where tangible and intangible values are integrated, where cultural and natural characteristics of place are subject to change over time, and where people and places are interconnected. They are places characterized by use and continuity. This research examines the factors that contribute to the identification, conservation and management of cultural landscapes and to an understanding of how their conservation affects the critical relationship between culture and nature. It proves that while scholarly literature and institutional guidelines are available to help define, identify and evaluate cultural landscapes, research is weak with respect to their management. Despite the broadening of the concept of cultural landscape during the last two decades, there is a critical need to further develop this concept and to integrate it into a values-based management approach as well as into national legislation. The study of two examples from Canada and Iran highlights the shortcomings of the application of the UNESCO World Heritage Convention and demonstrates the complexities of identifying, designating and conserving cultural landscapes at national and international levels. Head-Smashed-In Buffalo Jump and Takht-e-Soleyman illustrate how a lack of recognition of values and inadequate and often inappropriate legal frameworks at different levels of government have resulted in significant management and conservation challenges. The research concludes that cultural landscapes are not protected and are under threat because of a misapplication of theory in practice, and because of a lack of understanding of the concept of cultural landscape and its categories in local and cultural contexts. Improvements could be achieved by recognizing the complexities and challenges of large-scale, multi-layered cultural landscapes, and by introducing new approaches and perspectives into the broader field of heritage conservation. The development of reference models would illustrate the challenges of applying the concept of cultural landscape in conservation practice and of promoting the application of an integrated and holistic approach for similar heritage properties with tangible and intangible, and cultural and natural values. The conceptual framework that emerges out of this study is intended to be ultimately applied in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".