The Anglicization of Names Amongst the 2nd Generation of Sri Lankan Canadian Tamils in Toronto: an Autoethnographic Inquiry
Bibliographic record
Abstract
This paper presents an autoethnographic analysis of experiences related to the Anglicization of my name as well as those whom I have encountered within the Sri Lankan Tamil community here in Toronto. Through an in-depth analysis of articles related to the historical Anglicization of immigrant names as well as an analysis of the autoethnographic piece, I argue that the Anglicization of Tamil names amongst the 2nd generation of Sri Lankan Tamils living in Toronto is due to the internalization of deficiency (Y.Guo, 2015) and is done to maintain one’s habitus. This deficiency internalization is experienced through socialization in various social fields such as academia and the labour market where it can be understood that members of the 2nd generation, as well as immigrants in general, are taught early on that their cultural dispositions are inferior (S. Guo, 2015, p.11). These will be explored in greater depth throughout this study.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".