Factorial invariance of the abridged version of the Explicit Discrimination Scale among adults living in southern Brazil
Bibliographic record
Abstract
OBJECTIVE: The Explicit Discrimination Scale (EDS) was developed to assess experiences with discrimination in Brazilian epidemiologic surveys. Though previous analyses have demonstrated that the EDS has good configural, metric, and scalar properties, its invariance has not yet been investigated. In this study, we examined the factorial invariance of two abridged versions of the EDS, according to skin color/ethnicity, sex, socioeconomic status, and their intersections. METHODS: Data from the EpiFloripa Adult Study were used, which include a representative sample of adults residing in a state capital of southern Brazil (n=1,187). Over half of the respondents were women, and around 90% identified as white; the mean age of the participants was 39 years. Two abridged versions of the EDS were analyzed, with seven and eight items, using Multigroup Confirmatory Analysis and the Alignment method. RESULTS: The two versions of the scale may be used to provide estimates of discrimination that are comparable across skin color/ethnicity, sex, socioeconomic status, and their intersections. In the seven-item version of the scale, only one parameter lacked invariance (i.e., threshold of item i13 - called by names you do not like), specifically among black respondents with less than 12 years of formal education. CONCLUSION: The EDS may provide researchers with valid, reliable, and comparable estimates of discrimination between different segments of the population, including those at the intersections of skin color/ethnicity, sex, and socioeconomic status. However, future research is needed to determine whether the patterns we identified here are consistent in other population domains.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".