Bibliographic record
Abstract
The slogan is a special means to spread languages. It employs brief and short words to inspire, appeal or stop people to perform things. In the long history of China, the public slogan once played a significant role under a given historical background. It can be counted as an important approach of propaganda, involving the areas of politics, economy, culture and society, in the modern and contemporary China. Chinese slogans have strong characteristics of the Chinese language. In nearly a decade relevant studies about the type, translation, rhetoric and language features of Chinese public slogans emerge in an endless stream. Unfortunately, most of such studies are performed by scholars in the Chinese linguistic circle; the research which is based on English rhetoric or related theories of stylistics is quite rare. Therefore, in this thesis the author will analyze the stylistic features of Chinese public slogans from the perspective of English semantic features. Through the collected 50 slogans, we can find: (a) syntactically, these slogans are composed of simple words and symmetrical sentences; (b) semantically, these slogans are easy to understand and vary in their language styles; (c) rhetorically, these slogans mainly employ figures of speech, such as parallelism, personification, antithesis, etc..
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".