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Record W2052553459 · doi:10.1177/154193120104500435

Putting Cognitive Work Analysis to Work in Industry Practice: Integration with ISO13407 on Human-Centered Design

2001· article· en· W2052553459 on OpenAlexaff
Shinichiro Hori, Kim J. Vicente, Yujiro Shimizu, Isao Takami

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2001
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWork (physics)Computer sciencePerspective (graphical)Systems engineeringIndustrial designEngineering managementEngineeringSoftware engineeringRisk analysis (engineering)Artificial intelligenceBusiness

Abstract

fetched live from OpenAlex

This paper investigated how to conduct concrete design for industrial systems to conform to the recently-established Human-Centered Design standard ISO13407 (ISO). Referring to Sanderson et al.'s (1999) System Life Cycle (SLC) research, we adopted the Cognitive Work Analysis (CWA) framework as one useful and concrete designing method for industrial systems design to conform to ISO. Based on this idea, we compared three approaches ISO, SLC, and CWA and surveyed the match between ISO and CWA. From this study, we learned that integrating this research would provide great benefits to expand and improve the ISO concept for industrial systems, to give SLC a concrete methodological perspective, and to facilitate CWA technology transfer to practical design. As a result of these benefits, designers for industrial systems can get more structured ways of conducting adequate designs to help workers adapt to any demands and also to conform to ISO. An industry case study with a design example of a pump plant system supported our ideas. Also, we could confirm that much of the required information for ISO could be extracted by CWA. Therefore, the CWA models would not only be useful tools for industrial system designers in all of the SLC stages, but they also would be helpful to make our designs conform with the ISO standard.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.354
Teacher spread0.294 · 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 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

Citations4
Published2001
Admission routes1
Has abstractyes

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