In Praise of Ressentiment: Or, How I Learned to Stop Worrying and Love Glenn Beck
Bibliographic record
Abstract
American political discourse in the summer of Tea Parties and Glenn Beck’s tears is suffused with ressentiment. Wendy Brown’s “wounded attachments” characterize feminists, multiculturalists, anti-tax protesters and Christian conservatives alike, in that all base their appeal in an identity that is thoroughly constituted by a foundational wounding. The perceived injury becomes not just a point of origin but provides a continuing impulse to fixate on perceived wrongs as the basis for political community and action. Rather than contesting or lamenting this, I want to consider ressentiment as something defensible more for its effects than for its nature, and specifically I want to imagine what can be said in its defense from the vantage of a renewed Left in the United States. In this I follow Madison’s defense of faction in my method, where Madison in the Federalist shows both the dangers of faction as well as how it may be put to good use. I differ from Madison in that I will not argue that ressentiment is sown into the fabric of human nature, as the question is not so much whether it is our inescapable doppelganger as it is to know what we should do with the ressentiment that we already have. This paper explores a Machiavellian reclamation of ressentiment “well-used,” argues that its strategic employment in past liberation struggles has been crucial to the successes of the Left, and proposes several specific tactics in political rhetoric and mobilization.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".