Bibliographic record
Abstract
and countries in the European Union will be considerably more ethnically diverse by the middle of the 21st century than they are today (Eurostat, 2010; Statistics Canada, 2010; U.S. Census Bureau, 2008). The Canadian Broadcasting Corporation (CBC) recently ran a report stating that “about one-third of Canada’s population—up to 14.4 million people— will be a visible minority by 2031 ” (CBC, 2010). In the United States, CNN recently ran the story “Minorities Expected to Be Majority, ” which highlighted that “by 2050, 54 % of the population will be minorities ” (CNN, 2008). Conservative Pat Buchanan (2004) laments that “the Amer-ica of our grandchildren will be another country altogether, a nation unrecognizable to our parents.... White Americans will be a minority, 49 percent, and falling. When we all belong to ‘minorities, ’ what will hold us together? ” Do Buchanan’s expressions of alarm reflect a wider sense of threat that White Americans experience when considering growing ethnic diversity? Given that people are being made aware of impending demographic changes, it is important for social psychologists to examine how knowledge of these changes might affect current intergroup relations. In two studies—one in the United States and one in Canada—we look at the issue of growing ethnic diversity in terms of how expecting these changes might affect Whites ’ feelings toward ethnic minorities. Demographic Changes
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.313 |
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".