Microaggressions: Clarification, Evidence, and Impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it