Embedded Public Reasoning: A Response to Jonathan Haidt’s The Righteous Mind
Bibliographic record
Abstract
Jonathan Haidt is a moral psychologist whose influential book, The Righteous Mind: Why Good People are Divided by Politics and Religion, explains the origins of our political disagreements. The aim of the book is to encourage understanding and civility in our public life. Deliberative democrats also have a significant stake in understanding the sources of our disagreements and see rational deliberation as the key to civility and democratic legitimacy. However, Haidt’s empirical studies give reasons to suggest that the “faith” of deliberative democrats in reasoning may be misplaced, particularly as that faith tends be inflected in terms of a “Kantian” moral psychology.This article analyzes four different explanatory “stories” that Haidt weaves together: (1) a “causal” evolutionary account of the development of morality; (2) a “causal” story about the psychological mechanisms explaining human action; (3) a “causal” story about the historical and cultural determinants of our political attitudes; and (4) a “normative” story about the grounds and justification of human action. The article then examines these stories to discern how deliberative democrats might rearticulate their conception of public reasoning, and their normative hopes for it, in light of Haidt’s findings by introducing the “embedded” conception of public reasoning.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.023 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 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".