Undoing Privilege: Unearned Advantage and Systematic Injustice in an Unequal World (Book Review)
Bibliographic record
Abstract
Social justice studies and activism are often focused on the role of oppressed groups in challenging their oppression, while privileged groups and their responsibility in reproducing inequality tends to be overlooked.In Undoing Privilege, Pease centres the narrative around the operation of privilege to raise attention to its pervasive and unchallenged nature.The book is intended for readers who possess some form of privileged identity and who are willing to grapple with the impacts of their unearned advantages on the lives of others.The book interrogates the contradictions associated with privilege, which include the complexity of privileged people participating in struggles for social justice, as well as the author's positionality as a white, heterosexual, cisgender, able-bodied man writing a book about privilege.Pease identifies a gap in discourses about privilege in popular culture and activism, where analyses tend to focus on privilege at the individual level and neglect its structural and systemic dimensions.He also identifies a gap in academia, where studies are mostly focused on oppression or on the privilege of the elite.The book aims to address this gap by offering a nuanced analysis of privilege that examines how it operates at the individual, cultural, and structural levels.The central claim of Undoing Privilege is that unearned privilege operates to maintain inequality and oppress people who are unable to access it.Pease suggests that the inequality produced by privilege is often implicit because privilege tends to be invisible to people who possess it,
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".