Modeling and Theorizing in Psychology: Autoepistemology and Epistemic EDI (Equity, Diversity, and Inclusion) as Tools for Challenging Racism
Bibliographic record
Abstract
After challenging models of human nature and psychological science, a series of interrogations are proposed that aid in improving the practice of theorizing in psychology, specifically in regard to the topic of race. The program of autoepistemology is defined as the study of how “my” knowledge is connected with histories, cultures and societies as well as with the academic communities in which “I” participate, and with “my” personal cognitive, affective and motivational preferences and experiences. Autoepistemology includes reflections about the relationship between psychological knowledge on race, on the one hand, and intellectual and cultural traditions, horizons, and practices, on the other hand. It is argued that theorizing on race is strengthened when addressing equity, diversity and inclusion (EDI) as epistemic categories in all contexts of the research process. Epistemic EDI, which includes an interrogation of power, together with institutional and educational EDI, has the potential to dismantle racism in psychology. An approach to theorizing about human groups that avoids the pitfalls of White epistemologies is proposed.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.054 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".