A study on Chinese linguistic landscapes from the perspective of positioning theory
Bibliographic record
Abstract
Positioning theory is an analytic framework to explore the identity-forming of interlocutors in communications. In this study the author adopts positioning theory to analyze the Chinese linguistic landscapes in the city of Winnipeg in order to explore the positional identities of the Chinese population in western societies. The results show that Chinese Winnipeggers: a) assign high values to the English language but probably not equally to the French language; b) tend to accept western cultures and learn the English language; c) like to express their ethnic identities and inherit and carry the traditional Chinese cultures forward; d) attach importance to the marketing value of English and some Asian languages (like Korean and Vietnamese); e) are supported by some local businesses regarding languages; f) gradually replace the “hostile relationships” between old and new Chinese immigrants with “friendships and partnerships”; and g) identify Chinese newcomers as investors who need and are eager to buy educational products. The writer of this study exposes the power dynamics between the Chinese immigrants and mainstream society in Winnipeg, reveals the relationships between various Chinese sub-groups created during differing waves of immigration, and builds connections among time, space, and people. The functions and roles of linguistic landscapes in language education are discussed in the study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".