Bibliographic record
Abstract
According to Pikhart, people with Chinese ethnicity usually use English names while living and studying in North America to foster connections and relatedness to the local culture, to help them integrate faster into mainstream society. This study aims to investigate whether name-based microaggression and name-based group-specific stereotypes towards the Asian population are rooted in North American culture. In a research done by Arai, Bursell, and Nekby in 2008, researchers compared employer’s attitudes towards CVs of equal observable quality between Arabic names and Swedish names in Sweden, it was found that Arabic men suffered most from name-based discrimination by receiving fewer interview offers, the results of employers’ subconscious decision-making show that implicit name-based microaggression is a serious problem that deprives competent individuals of having the equal opportunities they deserve. In order to address this problem, interventions from different aspects can undermine it, whether in the workplace, at school, or in renting market. It is crucial for organizations such as schools, companies, and government to implement measures to enhance people’s awareness of name-based discrimination.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".