Hot under the collar
A latent measure of interstate hostility
Bibliographic Data
| ID | 3918340 |
|---|---|
| Authors | Zhanna Terechshenko (0000-0001-7585-1369, New York University, corresponding author) |
| Year | 2020 |
| Volume | 57 |
| Issue | 6 |
| Pages | 764-776 |
| Publication date | 2020-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Peace Research (JOURNAL) |
| Journal identifiers | ISSN: 0022-3433 • E-ISSN: 1460-3578 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0022343320962546 |
| OpenAlex | W3103928587 |
| Language | EN |
| Citations received | 14 |
| References cited | 39 |
The majority of studies on international conflict escalation use a variety of measures of hostility including the use of force, reciprocity, and the number of fatalities. The use of different measures, however, leads to different empirical results and creates difficulties when testing existing theories of interstate conflict. Furthermore, hostility measures currently used in the conflict literature are ill suited to the task of identifying consistent predictors of international conflict escalation. This article presents a new dyadic latent measure of interstate hostility, created using a Bayesian item-response theory model and conflict data from the Militarized Interstate Dispute (MID) and Phoenix political event datasets. This model (1) provides a more granular, conceptually precise, and validated measure of hostility, which incorporates the uncertainty inherent in the latent variable; and (2) solves the problem of temporal variation in event data using a varying-intercept structure and human-coded data as a benchmark against which biases in machine-coded data are corrected. In addition, this measurement model allows for the systematic evaluation of how existing measures relate to the construct of hostility. The presented model will therefore enhance the ability of researchers to understand factors affecting conflict dynamics, including escalation and de-escalation processes
Bayesian probability · Benchmark (surveying) · Cognitive psychology · Construct (python library) · Data mining · Econometrics · Economics · Event (particle physics) · Hostility · International conflict · Latent variable · Measure (data warehouse) · Poison control · Political science · Politics · Reciprocity (cultural anthropology) · Artificial Intelligence · Computer Science · International Relations and Foreign Policy · Peacebuilding and International Security · Political Conflict and Governance · Psychology · Social Psychology
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| Unique citing works | 14 |
|---|---|
| Citations per year | 2,33 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |