GEOGRAPHICAL DISTRIBUTION OF MODERN AND POSTMODERN ACCOMMODATION SUPPLY: A CASE STUDY OF İZMİR (TURKEY)
Bibliographic record
Abstract
In this study, the geographical distribution of modern accommodation facilities and postmodern sharing residences in Izmir are compared. First, the geographical distribution of traditional facilities were observed by using the data of Izmir Provincial Directorate of Culture and Tourism. Afterwards, the locations of local residents who included their living spaces in CouchSurfing and Airbnb were determined. In this respect, 392 CouchSurfer were selected by random sampling method and they completed an online survey. The spatial data of Airbnb residences, on the other hand, were obtained from Airdna. Within the study, it is detected that modern supply is concentrated in districts such as Cesme (96-35%), Selcuk (19-7%) and Menderes (15-5%) which are characterized by sea-sun-sand and material culture, whereas postmodern supply is concentrated in Karsiyaka (181-12%), Bornova (161-11%), and Buca (61-4%) which are outside the traditional accommodation corridor and focus on non-material culture. On the other hand, the supply of Airbnb provides accommodation in the districts such as Cesme (339-32%), Urla (46-4%) and Karaburun (44-4%) where traditional facilities are generally concentrated. The geographical distribution of modern (69-25%) and postmodern (235-16%) supply in central Izmir shows parallelism only in Konak. When this similarity on the district is examined on a quarter level however, it turns out that the traditional facilities are concentrated in central and coastal quarters and adjacent quarters whereas the sharing residences are more dispersed and are located in the outskirts. In conclusion, the opening of residences in tourism by individuals adopting the sharing culture has brought about more balanced distribution of accommodation within the destination. It has provided a range of locations and prices as well as creating an alternative accommodation possibility and this situation allows post-tourists to go outside of tourist bubble and transition to areas in the back regions which are the actual residential areas for locals.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".