The Practical Forms and Cultural Implications of "City 不 (or not) City": A Study on Linguistic Hybridization in Chinese Social Media
Bibliographic record
Abstract
In the third quarter of 2024, "city 不 (or not) city", the internet buzzword with linguistic hybridization between Chinese and English, became popular on Chinese social media in a short period, which sparked widespread discussion. According to the research findings of this article, "city 不 city" is often used for the context containing 3 themes, including travel and recording experiences in relation to cities, local food culture as well as leisure time, and local culture about cities. In addition, "city 不city" is often used by users to present positive emotions in the related context and content. This could be driven by the individual's demand for genuine emotional expression or the desire to shape an ideal self-image. In terms of the cultural implications of "city 不city", on the one hand, "city 不city" can participate in the construction of the cultural identity of local cities. On the other hand, the advantages of "city 不city" in shaping shared meanings can help information receivers reduce the possibility of misunderstanding information. This can provide support and a positive impact on intercultural communication of the cultural identity of a country through the use of internet buzzwords with linguistic hybridization.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| 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".