Bibliographic record
Abstract
Microaggressions—small stereotypical slights that can accumulate into serious damage when repeated—capture a common experience of marginalized groups, yet skeptics raise significant worries about how to recognize and respond to them. Are microaggressions a unified type or a hodgepodge of discrete examples? Can perpetrators be blamed, especially if their microaggressions are unintentional and only become harmful when repeated by other agents? My dissertation engages with both these debates: it presents an account that unifies different types of microaggressions and demonstrates that blame is warranted. First, I clarify what’s aggressive and micro about microaggressions by positioning microaggressions and hate speech on a spectrum of escalating linguistic violence. I specify the similarities and differences between racial microaggressions and racist hate speech by building upon the work of legal critical race theorists Richard Delgado and Mari Matsuda. I then offer a new taxonomy of microaggressions. Psychologist Derald Wing Sue theorized two types of microaggression, microinsult and microinvalidation, but failed to clearly explain the distinction between them. I offer a new way to draw this boundary: microinsults perpetuate constrictive stereotypes, while microinvalidations risk reigniting dangerous associations that have in the past been used to justify violence. Having answered the descriptive challenges, I turn to normative questions. First, I counter the growing trend of comparing microaggressions to collective harm problems, like climate change. While I agree that microaggressions contribute to collective harm, I demonstrate that microaggressions are also individually wrongful, and thus collective harm models do not fully capture moral responsibility for microaggressions. Second, I introduce my own account of moral responsibility for microaggressions, based on concepts drawn from tort law. Just as I can object to incursions onto my property, so too can I object to microinsults that threaten my mental capacities, and just as I can object when I am harmed because a business failed to take sufficient safety precautions, I can object to perpetrators of microinvalidations who failed to foresee the harm their careless words could cause. These comparisons to tort law help us to capture the relational wrong committed by microaggressors, and also points us towards the appropriate remedy: apology.
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.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".