Examining the Unexamined: Investigating Tamil Canadians experiences of racial/ethnic discrimination and wellbeing
Bibliographic record
Abstract
Canada was the first country to adopt a national multicultural policy aimed at allowing all residents to feel a sense of belonging (Government of Canada, 2015), however racism continues to exist (Nelson & Nelson, 2004). Because racism can have a significant negative impact on mental and physiological health (Williams & Mohammed, 2009) it represents an important social issue. While evidence for racial disparities in Canada exists, reliable health-related research on racialized populations is rare (Nestel, 2012). This is also true for South Asians who represent one of the largest and fastest growing (Rahim, 2014) minority groups. Amongst Tamils, a distinct South Asian ethnic group and one of the largest Canadian refugee groups (Sandercock, Dickout, & Winkler, 2004), to our knowledge there is no study examining experiences of discrimination despite evidence of its existence (Beiser et al., 2003; Poolokasingham et al., 2014). The purpose of this study is to examine: 1) the nature of perceived racial/ethnic discrimination experienced by Tamils, 2) the factors that may contribute to these experiences (skin tone, accent, and ethnic density), 3) the relationship between these experiences and health outcomes, and 4) role of protective factors (social support, acculturation and ethnic identity). Findings suggest Tamils experience racial microaggressions and macro discrimination. Increases in both forms discrimination were correlated with decreased wellbeing and ethnic identity was correlated with decreased psychological distress. Skin tone, accent and ethnic identity did not account for increases in discrimination. Social support, acculturation, and ethnic identity did not moderate the relationship between discrimination and health. A descriptive model for understanding these relationships is presented and implications are described.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".