Bibliographic record
Abstract
Abstract This paper provides a simple dynamic model that explores the interdependence and dynamic properties of hate, violence and economic well-being. It shows that a time-dependent economic growth process that affects the evolution of hate can yield a long-run steady state, but this steady state will not be free of hate and violence. Moreover, we show that better (long-run) economic conditions do not necessarily result in lower equilibrium levels of hate and violence. We also show that, under reasonable conditions, cycles of hate and violence cannot occur. Consequently, the dynamic properties of hate and violence alone cannot result in cyclical (net) economic well-being patterns. While stable and unstable equilibria are possible, the most likely equilibrium is a saddle point. Given its nature, we can view the paper as an example of a formal model for the ideas of the “dynamical system” literature in psychology. Although the paper does not discuss policy decisions, it identifies potential instruments for policymakers to achieve better steady states and greater stability. Finally, we provide two fully nonlinear multi-dimensional numerical examples (in an appendix) to demonstrate the implications of various psychological attributes, sensitivity to economic conditions, externalities, violence and small equilibria perturbations regarding the nature of the steady state and stability of the equilibria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".