Experiences of second-generation students of Punjabi Sikh ancestry in the British Columbia school system
Bibliographic record
Abstract
Punjabi Sikhs migrating to Canada form a disproportionately large population of the migrants from South Asia. There has been limited research or current literature on the schooling experiences of the second-generation children of these migrants despite the large numbers of this group migrating to Canada. The effects of minority status within the K-12 British Columbia school system regarding school experiences of second-generation students of Punjabi Sikh descent are presented throughout this research process. The investigation focused on the research participants’ perceived school experiences and whether there were differences based on the school type’s demographic composition of responders. I categorized these school types into three: small minority population, large minority population, and large majority population. I hypothesized that schools with large majority populations would have greater perceived satisfaction with school experiences. I found that I could further analyze by subscale and total scale groupings, based on my original correlational analysis. I found differences on school experiences (SE) and home experiences (HE) subscales based on school type, school type being differentiated by schools with a minority population, a large minority population, or a large majority population of the responder demographic of second-generation students of Punjabi Sikh descent. I found that responders from small minority population schools and large minority population schools showed a statistically significant difference in responses than responders from large majority population schools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".