MétaCan
Menu
Back to cohort
Record W1969127334 · doi:10.1177/154193120004400418

A Work Domain Analysis for Network Management

2000· article· en· W1969127334 on OpenAlexaff
Catherine M. Burns, Ed Barsalou, Cynthia Handler, Johnson Kuo, Kevin Harrigan

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2000
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceHierarchyAbstractionDomain (mathematical analysis)Interface (matter)Work (physics)Task (project management)Distributed computingSoftware engineeringHuman–computer interactionSystems engineeringEngineeringOperating system

Abstract

fetched live from OpenAlex

Computer networks are pervasive and critical to the operation of today's businesses. While most of us take computer networks for granted, network managers who must configure, operate, and fix faults in computer networks face a daunting task that becomes more complex everyday. Ecological interface design has been shown to be a promising approach for complex systems such as power plants or petrochemical systems. Computer networks are a very different work domain characterized by rapid technological change, extreme decentralization, and the existence of “soft” components. This paper presents the first work domain analysis of this new domain creating an abstraction hierarchy for general signal transfer systems and general computing networks. It shows how the abstraction hierarchy can be applied to this new domain and presents the initial work on an ecological interface design for network management.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0020.002
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations14
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicAdvanced Software Engineering MethodologiesFrench-language works237,207