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Record W1534876870 · doi:10.1017/cbo9781139208727

Mercenaries in Asymmetric Conflicts

2012· book· en· W1534876870 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

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.863
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
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