Bibliographic record
Abstract
Hakka emigration has created many smaller communities worldwide; where some groups continued their migratory journey. One such example is the Hakkas, who first migrated to Calcutta and then moved on to Vienna and Toronto, clustering in a close-knit social network. In various sessions, Viennese Hakkas of all age groups were interviewed for their lifestories and linguistic practices. (a) The linguistic competence of the migrants includes Hakka, English and Indian (Hindi, Ben¬gali) but often rather little German; Hakka is important at the workplace (Chinese restaurants) and is transmitted in families; Indian helps establish professional relationships with Indian migrants. (b) The social network is rather closed to Hakka friends from Calcutta or from other places. All Hakkas closely cooperate and usually have only few outside contacts. They consider Calcutta as their old homeland to which they return for Chinese New Year. (c) The younger generation consists of weak speakers of Hakka who are fully integrated into Austrian culture, but also maintain contacts to Toronto and love to visit friends and family in India. To conclude, the Indian Hakkas of Vienna are an interesting example of a two-step migration which first converted some Chinese into Indians, and then planted this Indian subgroup into Europe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".