Bibliographic record
Abstract
, Scott Lilienfeld critiqued the conceptual basis for microaggressions as well as the scientific rigor of scholarship on the topic. The current article provides a response that systematically analyzes the arguments and representations made in Lilienfeld's critique with regard to the concept of microaggressions and the state of the related research. I show that, in contrast to the claim that the concept of microaggressions is vague and inconsistent, the term is well defined and can be decisively linked to individual prejudice in offenders and mental-health outcomes in targets. I explain how the concept of microaggressions is connected to pathological stereotypes, power structures, structural racism, and multiple forms of racial prejudice. Also described are recent research advances that address some of Lilienfeld's original critiques. Further, this article highlights potentially problematic attitudes, assumptions, and approaches embedded in Lilienfeld's analysis that are common to the field of psychology as a whole. It is important for all academics to acknowledge and question their own biases and perspectives when conducting scientific research.
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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 | 0.058 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".