Tarihi kentsel peyzaylarda sosyal sürdürülebilirlik aracılığıyla kültürel mirasın korunması: Sakarya mahallesi için ilkeler, stratejiler ve öneriler.
Bibliographic record
Abstract
Historic urban landscapes comprehend the natural and built environment as well as the social and cultural practices, values, meanings and identification of the communities that interact with them. In this sense, historic urban landscapes, with their entire tangible and intangible characteristic, have been shaping and evolving continuously by the communities and have been transferred to present generations. Thus, providing the sustainability of the communities is necessary to provide the continuance of historic urban landscapes in heritage conservation. However, some major forces such as rapid urbanization, industrialization, and mass tourism endanger the relationship between communities and historic urban landscapes as well as their sustainability to future generations. The aim of the study is to propose an alternative conservation approach in historic urban landscapes by linking the heritage conservation and the continuity of the communities that engaged with them via the concept of “social sustainability”. Following a multi-dimensional study on theoretical and onsite researches and evaluations, the study determines principles, strategies and proposals as a result. A representative example of historic urban landscapes that encounters changes in physical and social structure which endanger the sustainability of its current inhabitants is selected as the case study in this thesis: Sakarya Quarter in Ulus, Ankara. The study determines general guidelines, tools and process driven by the theoretical framework on the concept of social sustainability and its role in heritage conservation. In the light of this framework, the thesis reveals the general characteristic and values of Sakarya Quarter and the relationships with its inhabitants. As a result of general assessments on the site, the thesis proposes an alternative conservation approach for Sakarya Quarter by defining some principles, strategies and actions in order to provide the development of the community and ensure their sustainability for the next generations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".