One step further in mistreatment research: Assessing the scalability of the Explicit Discrimination Scale among Brazilian working‐age adult respondents
Bibliographic record
Abstract
Though the Explicit Discrimination Scale (EDS) has been subjected to extensive psychometric evaluation in Brazil, the instrument has yet to be comprehensively assessed among working-age adult respondents in the country. This study aimed to fill this knowledge gap. Data from around 1200 diverse members of a cohort investigation were used to examine: (1) the positioning of respondents along the continuum of the EDS latent trait; (2) how well the corresponding items represent the EDS construct map; and (3) the extent to which the EDS items follow their expected levels of intensity. We assessed these properties with Loevinger's H, Guttman errors, and Item Response Theory parameters. Findings suggest that two abridged versions of the instrument-but especially the eight-item EDS-may adequately arrange respondents along the latent trait continuum. Analyses also revealed that scale items are reasonably spread over the construct map, with some discrepancy between the expected levels of intensity and their empirical positioning in the corresponding plot. The shortened versions of EDS have good psychometric properties among Brazilian working-age adult respondents. In addition to examining the invariance of the EDS across multiple groups, future psychometric evaluations should assess the external validity of the scale.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".