Bibliographic record
Abstract
This article explores the historical context in which the concept of microaggression was produced and the psychological model that supported it. Microaggression has become a popular term used to describe the stress of minoritized groups beyond the experience of racism. This article presents a genealogical perspective informing the contemporary uses of the term. The concept of "microaggression" was developed by Black psychiatrist Chester Middlebrook Pierce (1927-2016), professor of psychiatry and education at Harvard University. Pierce played an important role in conceptualizing the relationships between the mental health of individuals and groups, and their environment. The career and story of Chester M. Pierce bear witness to the construction of the relation between racism and mental health in a therapeutic culture "in the making." Through a selective biographical account of the career and research of Pierce, this article examines what brought him to coin the term microaggression. It also considers the wider context of the political mobilization of behavioral sciences to understand and address social inequalities in the United States. The notion of microaggression was a conceptual tool used by Pierce to describe how racism is perpetuated as a psychological phenomenon and to help develop awareness of the need to propose defensive strategies. The contextualization of Pierce's research and achievements aims therefore to contribute to the history of American "therapeutic culture" and the discussion of the role that psychological concepts such as microaggression are assumed to play in the psychologization of power relations and everyday life. (PsycInfo Database Record (c) 2026 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".