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Record W1983225447 · doi:10.1177/154193120905300432

Work Domain Analysis for Establishing Collaborative Work Requirements

2009· article· en· W1983225447 on OpenAlexaff
Catherine M. Burns, Gerard Torenvliet, Bruce A. Chalmers, Stacey D. Scott

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2009
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsDefence Research and Development CanadaUniversity of Waterloo
Fundersnot available
KeywordsDomain (mathematical analysis)Work (physics)Task (project management)Context (archaeology)Computer scienceKnowledge managementProcess managementEngineeringSystems engineering

Abstract

fetched live from OpenAlex

We explored the use of Work Domain Analysis (WDA) in the context of maritime tactical picture compilation. In particular, we were interested in extracting requirements for collaboration within a naval task group. This work presents a novel use of WDA, as this analysis method has not previously been used specifically to examine collaborative work processes. The WDA identified unique domain views for various team positions and collaborative boundary objects shared within the team. This use of WDA in a group context provides new requirements for the design of collaboration tools.

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)0.007
metaresearch head score (Gemma)0.030
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0130.005
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.028
GPT teacher head0.315
Teacher spread0.288 · 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
GenreMethods

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

Citations17
Published2009
Admission routes1
Has abstractyes

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