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Record W4286685436 · doi:10.1080/01434632.2022.2102643

The linguistic landscape of Chinatowns in Canada and the United States: a translational perspective

2022· article· en· W4286685436 on OpenAlexaboutno aff
Ge Song

Bibliographic record

VenueJournal of Multilingual and Multicultural Development · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHybridityLinguisticsPerspective (graphical)SociologySemioticsCultural translationDiasporaEthnographyRelation (database)Ethnic groupLinguistic landscapeTranslation studiesAnthropologyGender studiesArtPhilosophyComputer science

Abstract

fetched live from OpenAlex

Chinatowns in Canada and the United States are marked by cultural hybridity, where the translation of various types, verbal and non-verbal, takes place to produce distinct urban meanings. On the basis of an ethnographic observation, this article reveals the role of translation in the signification and imagination of Chinatowns. Cultural diaspora in relation to multimodal translation is designed as a theoretical framework, under which linguistic, aesthetic and cross-cultural tensions are explained. It argues that the urban meanings of Chinatowns are generated through an omnipresent practice of translation enacted by the interplay of text, image and culture across time and space. In the meantime, Chinatowns have evolved from ethnic enclaves into cosmopolitan prototypes for future cities. A translational perspective on Chinatowns incorporates visual semiotics into verbal languages to unpack cross-cultural relations, which informs a great deal about the nature of translation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0290.023
Scholarly communication0.0090.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.022
GPT teacher head0.254
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2022
Admission routes1
Has abstractyes

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