Bibliographic record
Abstract
This study aims to identify how perceptions of racism in Canada are influenced by the consumption of American media. The current study hypothesized that: 1) individuals exposed to an American news story regarding racial discrimination (Group 1) would have a more favourable evaluation of Canada than those who were not exposed to the story (Group 2); 2) that participants who were people of colour (PoC) would have no significant differences in scores between the two groups, and; 3) that Canadians would overall rate Canada more favourably than America, but that this difference would be more pronounced in Group 1. Seventy-two (72) participants contributed data by completing one of two versions of a questionnaire, which had questions regarding satisfaction of one’s life in Canada, perceived ethnic diversity or acceptance in Canada, perceived racism in Canada, and a comparison between Canada and the USA. One version opened with a short vignette describing an example of racism that had recently occurred in America (Group 1; 47 questions), while the other version did not (Group 2; 46 questions). A 2x2x2 analysis of the data revealed that PoC and those with a different national affiliation exhibited lower scores of perceived diversity in Group 1 than Group 2. Caucasian participants evaluated Canada more favourably than America in Group 1, whereas PoC rated Canada better in Group 2. Limitations of this study included sample size, diversity of the sample, reliability of the scales, and self-selection/self-report biases. Future research should aim to rectify these limitations and further explore the significant differences present in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".