Establishing construct validity evidence for regional measures of explicit and implicit racial bias.
Bibliographic record
Abstract
Large-scale data collection has enabled social scientists to examine psychological constructs at broad, regional levels. However, because constructs and their measures initially operationalized at the individual level may have qualitatively and quantitatively different properties at other levels of analysis, the validity of constructs must be established when they are operationalized at new levels. To this end, the current research presents evidence of construct validity for explicit and implicit racial bias at region levels. Following classic measurement theory, we examine the substantive, structural, and external evidence of construct validity for regional biases. We do so with responses from ∼2 million Black and White North Americans collected over 13 years. Though implicit measures typically demonstrate low retest reliability at the individual level, our analyses reveal conventionally acceptable levels of retest reliability at the highest levels of regional aggregation. Additionally, whereas previous meta-analyses find relatively low explicit-implicit correlations at the individual level, the present research uncovered strong explicit-implicit correlations at regional levels. The findings have implications for how we interpret measures of racial bias at regional levels. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.048 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".