Language, Space, and Identity in Migration: from the Local to the Global
Bibliographic record
Abstract
We started this book with two portraits of our immigrant participants, Frauke and Claudia, who are representative of the diversity of our participants. Frauke’s migration path started in a German settlement in Hungary, and brought her to Germany before she eventually moved to Canada as a teenager. She now lives in Kitchener-Waterloo and is constructing a space in which this migration path is still present, consciously or not, and in which she actively reconstructs and transplants elements of it. In doing so, she makes use of local resources such as German cafés, German ethnic clubs, and other German speakers, though they may not all speak the same German variety that she does. Now near retirement age, her adjustment to Canada can be seen in the ways she creates spaces through a mixed code, the local references she makes, and the ways in which her language attitudes and ideologies correspond to those of German speakers living in Canada rather than European Germans. She has made a choice for Canadian citizenship but ‘my heart is German,’ she says, which indicates the deep emotional traces connected to the languages she has encountered in her life. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".