Bibliographic record
Abstract
Traditional Canadian historiography, at least until the Charter of Rights and Freedoms of 1982 spurred a more multicultural mandate, has excluded immigrant and aboriginal stories told by themselves. There are Chinese voices in the academy now, but they speak only from a post-World War II perspective. This is a laudable step towards recognizing racial diversity, but racial diversity is not only visual (red, yellow, black, white and brown) but also linguistic. Within each ethnic category there is a sonic diversity. The post-war presentation of Chinese in Canadian history imposes the Cantonese dialect, but in reality Chinese immigrants spoke and still speak a variety of different dialects. The voices of Chinese railroad workers are scarce in Canada in part because of an oversimplification of a complex sonic diversity. Most of them came from a region in South China that had numerous dialects. In particular, it is significant that the majority of labourers employed in building the Canadian Pacific Railway through the Rockies spoke dialects other than Cantonese. (Lai, 1977). Most Chinese who worked on the Canadian Pacific Railway in Alberta and British Columbia came from the region of Hlee Yep (aka Sze Yi or Sei Yup) in South China. Hlee Yep comprises Four Districts along the southern belly of China and the Pearl River Delta and is geographically the most distant region from Guangzhou (Canton). Hlam Yep, in contrast, comprises the three counties closest to Gong Jiu, and it consequently enjoys economic benefits derived from its proximity to the capital. Within Hlee Yep the county furthest from urban economic benefit was Hoisan (aka Taishan or Toysan), and consequently its people led the exodus to Gold Mountain. Residents from the Hlee Yep region comprised 62.7% of the Chinese CPR workforce. By contrast, only 2.5% of the same workforce came from Hlam Yep. The dialect that Hlam Yep people speak has been simplified to ‘Cantonese’ (or ‘provincial speech’) whereas the Hlee Yep dialects are generally referred to as Hoisan Wah, Hoisanese or simply Hoisan. Hoisan was the dominant sound in North American Chinatowns until the mid-1960s, but while statistics exist on Cantonese and Mandarin speakers in Canada today there are none on Hoisan speakers. Hoisan thus represents a significant gap in the soundscape of Canadian culture and history.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".