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Record W2899807817

Manifestations of the Egyptian Army’s Actions in the US Army’s 1976 Edition of FM 100-5 Operations

2018· article· en· W2899807817 on OpenAlexvenueno aff
Tal Tovy

Bibliographic record

VenueJournal of military and strategic studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsFirepowerDoctrineOperations researchSet (abstract data type)ManagementLawMilitary doctrinePolitical scienceHistoryAeronauticsComputer scienceEngineeringAncient historyEconomicsProgramming language
DOInot available

Abstract

fetched live from OpenAlex

Many studies emphasize the contributions of the lessons gleaned from the IDF actions in the Yom Kippur War in shaping the 1976 edition. This paper adds another aspect to these claims. The 1976 edition clearly reveals that the doctrine sanctified defense over offense and firepower over maneuvering. This was not how the IDF operated; even in strategic-level defensive battles (especially in the Golan Heights); it adhered to tactical and micro-tactical offenses. Furthermore, on October 8 the IDF set out on two multi-divisionary counterattacks, the first of which (Sinai) failed while the second (Golan Heights) succeeded. With this in mind, this paper will claim that it was in fact the Egyptian model that set a better example from which to learn and implement in the Central European arena. The analysis of the Egyptian model in contrast with the characteristics of the 1976 edition will stand at the core of this paper. In other words, we shall analyze how the Egyptian war plans (up to October 14) execution had manifested in American doctrine.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.373
Teacher spread0.275 · 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 designQualitative
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

Citations1
Published2018
Admission routes1
Has abstractyes

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