Bibliographic record
Abstract
Slips of the tongue, unwitting favoritism, and stereotyped assumptions are just some examples of microaggression. Nearly all of us commit microaggressions at some point, even if we don’t intend to. Yet over time a pattern of microaggression can cause considerable harm by reminding members of marginalized groups of their precarious position. The Ethics of Microaggression is a much needed and clearly written exploration of this pervasive yet complex problem. What is microaggression and how do we know when it is occurring? Can we be held responsible for microaggressions and if so, how? How has social media affected the problem? What role can philosophy play in understanding microaggression? Regina Rini explores these highly topical and controversial questions in an engaging and fair-minded way, arguing that an event is a microaggression precisely because it causes a marginalized person to experience an ambiguous encounter with oppression. She illustrates her argument with compelling examples from media, politics, and psychology and explains the significance of essential concepts, such as media representation, reparative renaming, and safe spaces. The Ethics of Microaggression explains what microaggression is and offers strategies for combating it. Assuming no prior knowledge of the topic or philosophy, it demystifies a controversial and extremely important topic in clear language. It is ideal for anyone coming to the topic for the first time and for students in philosophy, gender studies, race theory, disability theory, and social and political philosophy.
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 | 0.008 | 0.010 |
| 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 | 0.062 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".