Place Images and Marketing Promotion of a City (Exemplified by Irkutsk)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine territorial marketing, a direction of regional policy, which is gaining increasing popularity across the globe; it emerged at the interface of marketing and socio–economic geography and is based on the notion of the uniqueness of each place. We discuss the methodological issues related to this direction and to its relevance to Irkutsk. A study is made of the use and prospects of the images of the city of Irkutsk as the tools for the promotion of the place and the attraction of migrants and tourists. The investigation was made at different geographical scales: regional (Irkutsk–Baikal); microgeographical toponymics, and statistical analysis of the individual perception of the city. Use was made of different investigation techniques: a multi–scale treatment of the same geographic phenomena against the background of the world, the country, the region and the agglomeration; analysis of the city’s recreational–geographical location as a variety of the economic–geographic location; comparison of street names according to the locality of the names, that is, the extent to which they are connected with the history and culture of the city as well as according to their popularity and content analysis of texts and images taken from the Internet and belonging both to tourists and to local residents, and images in the field of emotions. Some recommendations are made for the use of the images of the city in its marketing promotion. It is pointed out that the identified images were used in practice; in particular, in designing the historical № 130 Quarter in Irkutsk where timber representing one of the city images was widely used.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it