Replication Data for: The Damocles Delusion: The Sense of Power Inflates Threat Perception in World Politics
Bibliographic record
Abstract
How does power affect threat perception? Drawing on advances in psychological research on power, this paper finds that the sense of state power inflates threat perception. The sense of power activates intuitive thinking in the decisionmaking process, including a reliance on gut feelings and cognitive shortcuts like heuristics and prior beliefs. In turn, psychological IR research shows that these mechanisms tend to inflate threat perception. The powerful assess threats from the gut rather than head. Experimental evidence from the US and China, a re-analysis of a Russian elite survey, and a large-scale text analysis of Cold War US foreign policy elites lend support to this expectation. The findings help to psychologically reconcile enduring theoretical puzzles -- from underbalancing to overextension -- and generate entirely new ones, like the possibility that decisionmakers of rising, not declining, states feel greater fear. Together, the paper offers a "first image reversed" challenge to bottom-up accounts of psychological IR. Decisionmakers are also dependent variables shaped by the balance of power, with important implications for a world returning to great power competition.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.147 | 0.061 |
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".