Transformation of the urban linguistic landscape under the influence of social and cultural changes
Bibliographic record
Abstract
The study was conducted to analyse changes in ergonyms in urban landscapes under the influence of social and cultural transformations. The main focus was on the processes taking place in post-Soviet countries, in particular in Kazakhstan, where public policy, globalisation and decommunisation actively influence the change in the urban linguistic environment. The study examined changes in the names of objects in such cities as Almaty, Astana, Shymkent, Aktau and Pavlodar. In addition, examples of global megacities were studied, such as London, New York, Los Angeles, Montreal, Tokyo, Shanghai and Singapore, where the interaction of cultures and the impact of globalisation processes on the appearance of multilingual names in urban space were analysed. As part of the study, the literature was searched and analysed for keywords covering the topic of the linguistic landscape and globalisation processes. As a result of the study, it was found that changing names in cities of Kazakhstan is part of the overall process of decommunisation and the return of national identity. Analysis of the linguistic landscape of world cities has shown that globalisation also actively affects the names of commercial objects, contributing to the growth of multilingualism and adaptation to an international audience. The results obtained confirm the importance of language transformations as a reflection of social, cultural and economic changes in the urban space, which makes them an important indicator of modern urban processes. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0/ .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".