The praise of ressentiment: or, how I learned to stop worrying and love Donald Trump
Bibliographic record
Abstract
Abstract American political discourse in the era of Tea Parties, Donald Trump, and ‘#BiackLivesMatter’ is suffused with Nietzschean ressentiment. Left critical theorist Wendy Brown’s ‘wounded attachments’ characterize civil rights protesters, multiculturalists, anti-tax activists, and Christian conservatives alike: all are grounded in an identity thoroughly constituted by foundational wounding, which then provides a continuing impulse to fixate on perceived wrongs as the basis for political community. Rather than lamenting this, however, I defend ressentiment from the vantage point of a renewed Left in the United States. This paper explores a strategic reclamation of ressentiment ‘well-used,’ argues that its employment in past liberation struggles has been crucial to the successes of the Left, and proposes several specific tactics in political rhetoric and mobilization, including: (a) embracing victim/enemy narratives, (b) cultivating anger, and (c) deploying effective lies rather than ineffective truths.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.012 |
| 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".