Aferindo experiências com discriminação em múltiplos grupos sociais: análise de invariância da Escala de Discriminação Explícita em estudantes universitários
Bibliographic record
Abstract
This study evaluated the ability of the Explicit Discrimination Scale (EDS) to produce comparable estimates among respondents according to gender, color/race, and socioeconomic status. Analysis was based on data from two studies with students from Brazilian public universities. An abridged version of the EDS with eight items was evaluated by the alignment method. Findings indicated violation of invariance between color/race and gender groups. Reports of discriminatory experiences had better comparability between socioeconomic status strata. This study showed that EDS should be used with caution, especially to compare discrimination estimates between respondents of different colors/races and genders. The observed violation of invariance reinforces the need for additional research examining whether such a scenario persists in larger and more diverse samples from Brazil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".