BOOK REVIEW: Dying of Whiteness: How the Politics of Racial Resentment is Killing America’s Heartland by Metzl, J. M. (2019).
Bibliographic record
Abstract
In Dying of Whiteness, psychiatrist Jonathan M. Metzl explores the impact of conservative policies on the health and perspectives of white Americans.Travelling through Missouri, Tennessee, and Kansas, Metzl discovered that negative attitudes toward gun control, social services, and government spending arise from a deeply embedded politics of "racial resentment" and that more white Americans are willing to vote against their own interests in order to protect what they consider to be their fundamental freedoms, such as the right to bear arms.Using a combination of field research and public health data, Metzl successfully argues that the resulting policy decisions, supported by many white Americans, are rooted in white supremacy and the history of racism in the United States.The book is organized into three main parts, each focusing on a single state and policy issue.The first part of the book explores pro-gun laws in Missouri and increasing rates of accidental injury and/or death by suicide involving firearms among the white population in the state.Working with a suicide loss survivor group, Metzl found that even though many of the group members lost loved ones to firearm related suicide, the majority were willing to defend their freedom to own a gun.Most survivors in the group believed that their loved ones died because they failed to see the warning signs associated with depression and suicidality.When asked if they thought that guns made it easier to commit suicide, the majority upheld the belief that suicide prevention rests on recognizing symptoms and warning signs and do not believe that access to a firearm is a danger.Metzl connects these assertions to a broader politics of "racial resentment" and the history of racism and gun laws in the United States.He traces these ideas back to early colonial America, during which time firearms were "armaments of white upper-class power"
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".