The praise of ressentiment: or, how I learned to stop worrying and love Donald Trump
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it