Bibliographic record
Abstract
The book entitled “Exploring the Face of the City: Self-Representation Practices of Ukrainian Cities in the Industrial and Post-Industrial Age” (Kharkiv, 2021) was published at the end of 2021. It is devoted to the study of some theoretical and practical issues of urbanism. The main focus is on the symbolic spaces and cultural landscapes of five large cities of Eastern and Southern Ukraine — Dnipro, Donetsk, Zaporizhzhia, Odesa, and Kharkiv. The authors of the book are participants of the scientific project “CityFace: Practices of self-representation of multinational cities in the industrial and post-industrial age” (https://cityface.org.ua/), which was supported by the Canadian Institute of Ukrainian Studies. They consider these cities as centers of socio-cultural interaction and various innovations, as dynamic systems that are constantly changing, searching for their own relevant “face.” The central place in the book belongs to the study of the practices of self-representation of cities (the use of symbols and emblems, the formation of a pantheon of local heroes, the celebration of “significant” events, etc.), as well as options for articulating certain achievements, features of the city and its citizens, that is, ideas that are able to rally the urban community around certain ideological constructions (self-stereotypes). In this regard, the authors were interested in places of collective memory, commemorative and ritual practices, the process of symbolic coding and recoding of urban space. This approach allows us to get closer to understanding the specifics of micro-regional identities, which is considered a very relevant scientific task today. The publication is intended for everyone who is interested in the history and current state of socio-cultural processes in Ukraine. In this case, the introduction to this book is published, which has been translated into English, with the hope that this text will attract additional attention of readers to the book.
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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 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".