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Record W2065341059 · doi:10.1177/154193120705100457

Supporting Asynchronous Dialogs in the Communication of Army Operations Orders

2007· article· en· W2065341059 on OpenAlexaboutno aff
Philip J. Smith, Amy Spencer

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2007
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsnot available
FundersArmy Research Laboratory
KeywordsAsynchronous communicationUsabilityComputer scienceLikert scaleFace-to-facePlan (archaeology)MultimediaHuman–computer interactionPsychologyTelecommunications

Abstract

fetched live from OpenAlex

An empirical study was completed to study the use of an asynchronous multimedia communication tool to support dialogs during a joint forces military exercise. Ten captains, majors and colonels from Canada, France, Germany, Israel and the US who participated in the joint forces exercise had the option of using this multimedia communication tool whenever they felt it would help them to communicate information to commanders in other units. Two of the messages consisted of one-way communications. The remaining 13 were asynchronous dialogs. In these messages, the officers: • Made extensive use of pointing, drawing and embedded written notes • Used these asynchronous dialogs to detect and repair misconceptions that arose from live face-to-face briefings (6/13 dialogs) • Used these asynchronous dialogs to share expertise while developing a plan (13/13 dialogs). On Likert scales (1=strongly disagree; 7=strongly agree), the ratings for usefulness and usability were 6.2 and 6.4, respectively.

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)0.011
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.083
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

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