Renaming Practices: An Autoethnography of South Asian Names, Identity, and Belonging
Bibliographic record
Abstract
Names have been linked to various aspects of identity including ethnicity and language, and family. Studies have shown that the proper use of children’s names can reinforce and validate their identity. Research on renaming throughout Canadian history shows how these practices have the ability to dismantle and remove or alter identity. South Asians represent a large portion of Ontario’s population. However when their names do not conform to the dominant western culture, South Asians can be marginalized through racist microaggressions that contribute to their renaming. This paper is guided by critical race theory and Desi critical theory and explores the significance of my use of different names and why I chose and continue to choose these names. Using an autoethnographic approach I reflect on my name alteration experiences from my childhood and youth to explore how renaming practices can and do further marginalize minority children. Keywords: renaming practices; naming; South Asian; identity; belonging; childhood; autoethnography
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".