Bibliographic record
Abstract
A paradox haunts Lippmann’s critique of democracy running through his early work in Public Opinion up through The Public Philosophy . Liberal democracies, despite their claim to securing space for human dignity and freedom, can be sites of incredible cruelty. From the racial prejudices cutting through American politics, to the way Americans treated adversaries during war, democracy appeared to do little to vitiate the human propensity to inflict suffering upon others. This article examines Lippmann’s understanding of cruelty as a recurring feature of democracy and how he grappled with the question of how to curb the democratic public’s worst impulses. I argue that while Lippmann offers an expansive understanding of cruelty his analysis continually gravitates towards the role of cruelty in democracy and how the existence of mobs and demagogues represent democracy’s ever-latent potential for cruelty. Exploring his thinking further, I suggest there are at least two distinct views on the origins and dynamics of cruelty in his work – what I designate ‘callous’ and ‘joyful’ cruelty – influenced by James and Freud respectively. Finally, I contend that recognizing the gravity Lippmann assigns to the problem of cruelty is important because it can help us understand his puzzling turn to natural law in The Public Philosophy . Here I suggest Lippmann’s turn to natural law should be read as a radical pragmatist gambit in which the myth of natural law is mobilized to create a ‘tradition of civility’ aimed at curbing democratic cruelty. When we attend to this side of Lippmann we see a version of him that is less a conservative reactionary and more an anxious critic desperate to ward off the darker impulses of democracy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".