Bibliographic record
Abstract
Slow city movement has been firstly emerged in Italy with the purpose of eliminating the homogenous structure that the globalization has created in the cities. Slow city has been turned into an international network due to a philosophy providing sustainability of the city by improving the quality of individuals’ life. Turkey is also among the states which are the members of International Cittaslow Union. 11 districts have participated slow city movement starting with Seferihisar in Turkey. One of these districts is Gokceada constituting the case study. Gokceada has assumed the title of slow city by carrying out the criteria required for slow city in 2011. The aim of this study is to determine how the people’s perceptions and what their expectations towards citta slow phenomenon are. It is aimed to clarify the advantages and disadvantages of being a citta slow according to the public. The study has been conducted in the center of Gokceada through interview method. As a result of the research, it has been reached a conclusion that the people have knowledge about the Cittaslow concept. In addition, they have also assessed Gokceada being a citta slow as a positive development in terms of advantages provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".