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Record W1534876870 · doi:10.1017/cbo9781139208727

Mercenaries in Asymmetric Conflicts

2012· book· en· W1534876870 on OpenAlexaff
Scott Fitzsimmons

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLoyaltyDemocracyPolitical scienceBalance (ability)PsychologyEngineeringPublic relationsComputer securityLawComputer sciencePolitics

Abstract

fetched live from OpenAlex

Scott Fitzsimmons argues that small mercenary groups must maintain a superior military culture to successfully engage and defeat larger and better-equipped opponents. By developing and applying competing constructivist and neorealist theories of military performance to four asymmetric wars in Angola and the Democratic Republic of Congo, he demonstrates how mercenary groups that strongly emphasize behavioral norms encouraging their personnel to think creatively, make decisions on their own, take personal initiative, communicate accurate information within the group, enhance their technical proficiency and develop a sense of loyalty to their fellow fighters will exhibit vastly superior tactical capabilities to other mercenary groups. Fitzsimmons demonstrates that although the victorious mercenary groups occasionally had access to weapon systems unavailable to their opponents, the balance of material capabilities fielded by the opposing military forces had far less influence on the outcome of these asymmetric conflicts than the culturally determined tactical behavior exhibited by their personnel.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.038
GPT teacher head0.236
Teacher spread0.198 · 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 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

Citations16
Published2012
Admission routes1
Has abstractyes

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