Psychology Cannot Afford to Ignore the Many Harms Caused by Microaggressions
Bibliographic record
Abstract
In an ongoing debate, Scott Lilienfeld (2019) continues to question the merits and meaning of microaggressions research. Key issues include how to define microaggressions, whether microaggressions cause measurable harm, whether microaggression education is helpful, and defining the most important next steps in the microaggressions research agenda. I discuss the importance of understanding microaggressions in context and as they relate to pathological stereotypes about groups, given that this is critical to identifying them. I summarize some of the many longitudinal studies linking psychological and medical problems to experiences of everyday discrimination. In addition, the literature indicates that victims of microaggressions experience further harms when trying to respond to offenders, but there is little research to support any specific interventions, including those advanced by Lilienfeld. I discuss the importance of believing and supporting those reporting experiences of microaggressions. I conclude that there is a need for more research examining (a) how to reduce the commission of microaggressions, (b) how to best respond to offenders in the moment in a way that mitigates harm for all persons involved, and (c) how clinicians can best help those who are suffering as a result of microaggressions as the next frontier in this important work.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.051 | 0.084 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".