Pushing Back Against the Microaggression Pushback in Academic Psychology: Reflections on a Concept-Creep Paradox
Bibliographic record
Abstract
Echoing the 1960s, the 2020s opened with racial tensions boiling. The Black Lives Matter movement is energized, issuing pleas to listen to Black voices regarding day-to-day discrimination and expressing frustrations over the slow progress of social justice. However, psychological scientists have published only several opinion pieces on racial microaggressions, primarily objections, and strikingly little empirical data. Here I document three trends in psychology that coincide with the academic pushback against microaggressions: concept-creep concerns, especially those regarding expanded notions of harm; the expansion of right-leaning values in moral judgments (moral foundations theory); and an emphasis on prejudice symmetry, with the political left deemed equivalently biased against right-leaning targets (e.g., the rich, police) as the right is against left-leaning targets (e.g., Black people, women, LGBT+ people). Psychological scientists have ignored power dynamics and have strayed from their mission to understand and combat prejudice against disadvantaged populations, rendering researchers distracted and ill-equipped to tackle the microaggression concept. An apparent creep paradox, with calls to both reduce (e.g., harm) and expand (e.g., liberal prejudices, conservative moral foundations) concepts, poses a serious challenge to research on prejudice. I discuss the need for psychology to better capture Black experiences and to “tell it like it is” or risk becoming an irrelevant discipline of study.
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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 | 0.052 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.122 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.017 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 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".