Bibliographic record
Abstract
Immigrants land in Canada with great hopes and multiple dreams, but the General Social Survey 2009 shows that one-fifth of them face discrimination in various situations once they have arrived. Ethnicity, race, language, and religion are the major grounds of discrimination. In this paper, the experiences of discrimination of landed immigrants are compared with those of non-immigrants. A logistic regression analysis is used on GSS data to predict the probability of facing discrimination based on the socio-economic and demographic characteristics of a person. Separate models are prepared for landed immigrants and non-immigrants. Results show that immigrants are much more likely to face discrimination than non-immigrants. Visible minorities and younger persons face higher levels of discrimination compared to non-visible minorities and older persons. Irrespective of their gender, household income, language, region of domicile, and number of evening activities, landed immigrants have similar chances of facing discrimination; whereas, for non-immigrants, these characteristics make a significant difference in their experiences of discrimination. Key Words: Discrimination, immigrant, race, ethnicity, and human rights.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".