Revitalisation of the Main Street of a Distinguished Old Neighbourhood in Istanbul
Bibliographic record
Abstract
This study investigates the revitalisation of the main street of Beyoglu, which was the westernised part of Istanbul's CBD in the nineteenth century. Beyoglu started to develop in the sixteenth century with the introduction of embassy buildings of European countries. Its development reached a climax during the nineteenth century as a result of increased European trade and cultural influence, remaining the most distinguished quarter of Istanbul until the 1960s. Thereafter, it suffered from decay, disinvestment and abandonment as a result of later suburbanisation and the multi-centre development of Istanbul. Revitalisation of the quarter started with the pedestrianisation of the main street. This study investigates the functional transformation and changes in land prices along the main street and surrounding neighbourhoods after the pedestrianisation. The factors which effect land prices are investigated by the use of regression analysis. According to the results, access to mass transit is the most important factor. Besides its convenient central-city location, with easy access to the city's main transportation arteries, no doubt also its distinguished architectural character contributed to its revitalisation. Although the revitalisation of the main street as a cooperative movement of public and private sectors, effectively, it was a market-lead restructuring afterwards. At the same time, international companies opening up stores reflecting the globalisation movement increased the attractiveness of the main street. The results of the study can be used by urban planners, policy-makers and investors for the revitalisation of other historical neighbourhoods in Istanbul and other cities. For further research, hierarchical analysis of spatial impacts of revitalisation areas is suggested.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".