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Record W2134685422 · doi:10.5539/ach.v6n1p15

The Samar Counterinsurgency Campaign of 1899-1902: Lessons Worth Learning?

2013· article· en· W2134685422 on OpenAlexaffvenue
William N. Holden

Bibliographic record

VenueAsian Culture and History · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWonderPopulationPolitical economyPolitical scienceSociologyLawEconomyEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

During the Philippine-American War of 1899-1902, the United States Army’s most difficult, and renowned, counterinsurgency campaign was waged on the island of Samar. The Samareño insurgents had a well developed infrastructure and were merciless with those who collaborated with the Americans. The Samarnons made extensive use of the island’s rough terrain with heavy forest cover, and raised funds from the island’s hemp merchants. The Americans defeated the insurgents by separating them from the population. This was achieved through food restrictions, a strict policy of non-neutrality forcing the people to choose between them or the insurgents, by concentrating the population in camps, by providing benefits to those who cooperated with them, and through skillful use of intelligence to dismantle the insurgent infrastructure. This campaign may have been an excellent demonstration of the principles of counterinsurgency warfare but it inflicted substantial abuses upon the Samarnons and caused long term damage to the island’s economy. Given the atrocities associated with the campaign, and its detrimental effects upon the island’s economy, one must truly wonder if it provides lessons worth learning.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2013
Admission routes2
Has abstractyes

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