Sounding Chinese: Tracing the Voice of Early 20th Century to Present day Transnational Chinese
Bibliographic record
Abstract
Accent, that is differences heard in pronunciations, specifically the speech sound that identifies Chineseness is the departure point for this research into the construction of the identity of transnational Chinese. This question also frames pronounce, Meddling English, Oh Canada! and the six volumes of a project The Phrase Book of Migrant Sounds. The genesis of The Phrase Books of Migrant Sounds lies in phrase books written in the late-19th and early-20th centuries for migrants to North America. Of great interest is not only the fact that English phrases were translated into languages spoken in the southern parts of China (for example Toisanese and Cantonese) but that English words and phrases were transcribed using their corresponding pronunciations. These phrase books helped them articulate words of a language they had not heard before, the unfamiliar sounds made familiar, the alien brought closer to home. The research employs knowledge from an array of disciplines—cultural studies, sociolinguistics and anthropology to name a few—as well as archived sound recordings of late-19th century and contemporary transnational Chinese to map a sound history of transnational Chinese. It considers the experiences of those who call multiple places ‘home’ to challenge the singularity of transnational Chinese identity and to suggest that, rather than being monolithic, transnational Chinese identity is pluricentric and multiphonic. The thesis affords a link between one’s sense of identities, however changing though they are, with political, cultural and social experiences. My practice-based research Sounding Chinese: Tracing the Voice of Early 20th to 21st Century Transnational Chinese looks at how transnationals—those who call more than one place home and who modify the way they speak accordingly— conceptualize themselves.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".