Bibliographic record
Abstract
Abstract The recent ‘emotion turn’ in international theory is widely viewed as a cutting-edge development which pushes the field in fundamentally new directions. Challenging this narrative, this essay returns to the historical works of Walter Lippmann to show how thinking about emotions has been central to international theory for far longer than currently appreciated. Deeply troubled by his experience with propaganda during the First World War, Lippmann spent the next several decades thinking about the relationship between emotion, mass politics, and the challenges of foreign policy in the modern world. The result was a sophisticated account of the role of emotional stereotypes and symbols in mobilizing democratic publics to international action. I argue that a return to Lippmann's ideas offers two advantages. First, it shows his thinking on emotion and mass politics formed an important influence for key disciplinary figures like Angell, Morgenthau, Niebuhr, and Waltz. Second, it shows why the relationship between emotion and democracy should be understood as a vital concern for international theory. Vacillating between scepticism and hope, Lippmann's view of democracy highlights a series of challenges in modern mass politics – disinformation, the unintended consequences of emotional symbols, and responsibility for the public's emotional excesses – which bear directly on democracies' ability to engage the world.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".