MétaCan
Menu
Back to cohort
Record W2168714887 · doi:10.1177/0022002714547901

Desertion, Terrain, and Control of the Home Front in Civil Wars

2014· article· en· W2168714887 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Conflict Resolution · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité de Montréal
FundersYale University
KeywordsDesertionTerrainSpanish Civil WarCohesion (chemistry)IncentiveFront (military)Desert (philosophy)Political economyPolitical scienceControl (management)LawEconomyCriminologyDevelopment economicsGeographySociologyEconomicsCartographyManagementMarket economyMeteorology

Abstract

fetched live from OpenAlex

This article examines desertion in civil wars, focusing on the role of combatants’ hometowns in facilitating desertion. Analyzing data from the Spanish Civil War, the article demonstrates that combatants who come from hill country are considerably more likely to desert than combatants whose hometowns are on flat ground. This is because evasion is easier in rough terrain. The finding implies that the cohesion of armed groups depends on control, not just positive incentives, and that control of territory in civil wars goes beyond rebel–government contestation, and consists also of control behind the lines. The article bridges micro and macro approaches to civil wars by indicating the multiple uses to which individuals can put structural conditions like rough terrain. This helps to clarify the macro-level link between rough terrain and civil war. It also shows that micro-level research can profitably examine structural variables alongside individual characteristics and endogenous conflict dynamics.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.268
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it