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Record W7033568580

Scattering Chaff: Canadian Air Power and Censorship During the Kosovo War

2019· book· en· W7033568580 on OpenAlexaboutno aff

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2019
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStrategic bombingElement (criminal law)Power (physics)CensorshipPoliticsNational securityMilitary strategyAviation
DOInot available

Abstract

fetched live from OpenAlex

Most Canadians know little, if anything at all, about the role of the Canadian Air Force in the 1999 Kosovo Air War. Yet lives were at put at stake as mission dedication and military skill were pushed to the limit.Some of Canada’s most prominent journalists attempted to report on the war, but came away virtually empty handed. Daily briefings given at the National Defence Headquarters provided so little information most Ottawa journalists simply stopped going. The decision of the military to choke Canada’s news media was deliberate and based on a tactical and strategic rationale. Scattering Chaff explores the role of the Canadian Air Force in the bombing campaigns of the Kosovo Air War while examining the military’s interference with the news media attempting to report to the Canadian public. It explores the ways in which the military has come to manage the media as an element of operational security, mission focus, and of popular opinion. Drawing on in-depth interviews with the war’s Canadian participants and a treasure-trove of unpublished documents and photographs, this book is an unprecedented investigation of a little-known conflict and the forces that prevented it from being better known.

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)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0150.006
Scholarly communication0.0080.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.072
GPT teacher head0.284
Teacher spread0.212 · 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
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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueDirectory of Open access Books (OAPEN Foundation)Same topicDiverse Scientific and Economic StudiesFrench-language works237,207