Forms and Norms: \nTheorizing Immigration-Influenced Name Changes in Canada
Bibliographic record
Abstract
Canadian immigration and settlement practices have been altering individuals’ names since the mid-1800s. \nFrom the common explanations of immigration officials engaging in novel orthography as they completed \nforms, to families altering their names to make them easier for their neighbours to pronounce, a range of \ndominant cultural influences were at work. Today, these forces continue; they are evident in such technobureaucratic \nminutiae as maximum character lengths for permanent residents’ names, and in the decadelong \npolicy encouraging people with the religiously-significant Sikh names ‘Kaur’ and ‘Singh’ to remove \nthese names before applying to immigrate (CBC, July 2007). They are also heard in day-to-day \nintroductions as some newcomers choose to use common English or French names to present themselves, \nand to potentially make themselves more employable (Ng et al., 2007). \nWith these and other scenarios in mind I ask, in what ways and through what means do minority \nculture members and migrants to Canada change their names? What roles do legislation, policy and state \nregulated data collection procedures have in these shifts? How are names altered through less official \ninteractions? What implications do these name changes have for Canada as a nation-state? What are the \noutcomes in terms of nationalism or cultural pluralism?
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".