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Record W2152669766 · doi:10.1177/154193120805201921

Applying the Contextual Control Model (COCOM) to the Identification of Situation Awareness Requirements for Tactical Army Commanders

2008· article· en· W2152669766 on OpenAlexafffund
Simon Banbury, Sébastien Tremblay, Robert Rousseau, Kelly Forbes, Richard Breton

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2008
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsDefence Research and Development CanadaUniversité LavalProfessional Engineers Ontario
FundersDefence Research and Development Canada
KeywordsCommand and controlIdentification (biology)Control (management)Security controlsComputer scienceSituation awarenessOperations researchComputer securityProcess managementEngineeringRisk analysis (engineering)Systems engineeringAeronauticsTelecommunicationsBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Tactical command and control (C2) operations under conditions of complexity, uncertainty, stress, and time pressure impose significant cognitive demands on the Tactical Army Commanders' (TAC) ability to successfully prosecute their missions. The objective of the present study was to apply Hollnagel's (1998) Contextual Control Model (COCOM) to the identification of time-critical Situation Awareness (SA) requirements for TACs engaged in time-critical tactical C2 operations. SA requirements relevant to the successful completion of a broad range of tactical C2 missions – convoy escort, checkpoint security, combat, and cordon and search – were identified. These SA requirements were then prioritized into a subset of ‘critical’ SA requirements and then further analyzed in terms of classifying them into one of four COCOM control modes (i.e., strategic, tactical, opportunistic and scrambled). Through this analysis, areas that impact the development of decision aids, and other forms of decision support techniques, for TACs were identified.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.341
Teacher spread0.271 · 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 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

Citations9
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicHuman-Automation Interaction and SafetyFrench-language works237,207