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Record W1488893382

City Gods and Village Deities: The Urban Bias in Counterinsurgency Operations

2011· article· en· W1488893382 on OpenAlexaboutno aff
Eric Jardine

Bibliographic record

VenueMilitary review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsInsurgencyCommunismState (computer science)DemocracyGovernment (linguistics)Political scienceLawSociologyPolitical economyPublic administrationPolitics
DOInot available

Abstract

fetched live from OpenAlex

enemies are city gods, but we are village deities.1 - Peng Xuefeng, a Chinese Communist strategist HISTORY HAS DEMONSTRATED that rurally based insurgencies are often more successful against their counterinsurgent foes than insurgencies that emphasize urban operations. During initial stages of French-Algerian war in 1950s, two insurgent groups challenged French-the urban-based Movement for Triumph of Democratic Freedoms (MTLD) and rurally based Front of National Liberation (FLN). As conflict wore on, pressures brought to bear by French eventually destroyed urban MTLD. In contrast, largely because of its deep rural connections and organization, FLN withstood pressures of French military operations and eventually prevailed.2 Not only have rurally based insurgencies been more likely to outlast their urban counterparts, they have also been quite successful at defeating more powerful adversaries. The Chinese Communists' rebellion against Kuomintang suffered innumerable hardships in early years of insurrection when it focused on urban areas, but it later achieved startling successes when its strategic focus became rural. The root of Vietcong's success against United States in Vietnam was rurally based action, as was Mujahideen insurgency against Soviet Union in Afghanistan. The Taliban's current insurgency against International Security Assistance Force is also of a predominantly rural form. Contrary to rural focus of successful insurgencies, most counterinsurgencies emphasize control of major cities and use of urban-oriented operations. In Colombia, for example, state forces frequently control centers of large towns and cities, where municipal government buildings are located, but the state's authority evaporates as one moves further into countryside.3 Likewise, during Vietminh resistance to French, a government provincial chief noted, Vietminh had their areas, like Plains of Reeds, which we abandoned. Whatever Vietminh wanted to do [in those rural areas], we did not bother them.4 Similarly, in 2009, Canadian military emphasized a deployment of forces in area immediately in and around Kandahar City in Afghanistan.5 This urban bias in counterinsurgency operations is troublesome because it favors insurgency, and is welcomed and encouraged by guerrilla armies. Through purposeful harassment tactics by guerrilla forces, the government is systematically eliminated from countryside . . . The government is thus cut off from population.6 During 1916 Arab Revolt against Ottoman Turks, for example, T.E. Lawrence argued that Arab insurgents must not take Medina [a major city in Saudi Arabia]. The Turk was harmless there. We wanted him to stay at Medina and every other distant place, in largest numbers. The Turkish counterinsurgent was welcome to major cities and transit lines just so long as he gave [the insurgents] other nine hundred and ninety-nine thousandths of Arab world.7 Two questions emerge from this contradiction between urban operational bias of many counterinsurgencies and rural focus of successful insurgencies. Why is there an urban bias in counterinsurgency operations? And how does this bias influence conduct and resolution of a counterinsurgency? Answering these questions leads us to conclusion that control of urban areas, while necessary, is not sufficient to bring about a successful resolution to a counterinsurgency campaign. Urban Bias and Cost-Efficiency The concentration of counterinsurgency operations in urban areas is result of a myopic focus on issues of cost effectiveness and practicality. Such a focus leads counterinsurgencies to emphasize urban operations, often at expense of coherent rural plans. Control of local population is basic objective of both counterinsurgent and insurgent. …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.006
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.157
GPT teacher head0.323
Teacher spread0.167 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

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