Place Images and Marketing Promotion of a City (Exemplified by Irkutsk)
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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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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".