Urban Identity in (Post)Modern Cities: A Case Study of Kharkiv and Lviv
Bibliographic record
Abstract
This article aims to highlight the results of an empirical study of urban identity that was conducted by the author in Kharkiv and Lviv. The theoretical underpinnings of this research are based on the ideas of Manuel Castells and Zygmunt Bauman, as well as others. They assert that under the conditions of (post)modern society, groups which are involved in one way or another in the global post-industrial economy interpret cities and their relationship with them in a variety of ways—in other words, their definitions of urban identity vary. The author’s hypothesis is generally confirmed that groups will interpret their connection to a city in distinct ways: representatives of different groups will differ in their interpretation of the question of what it means to be an “urbanite” or a “true [insert city name]-ian,” in their ways of participating in the resolution of urban issues, etc. The unique features of the sampled Ukrainian cities (Kharkiv, Lviv) are described. The confirmation of the hypothesis serves as an argument in favour of considering urban identity in the context of an “imagined community.” Under such consideration, a city comprises not a “local community” but an aggregate of groups that consider the city to be “theirs” and defend their “right to the city” based on their individual image of the world, which depends on their social, cultural, and economic conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".