MétaCan
Menu
Back to cohort
Record W2084739116 · doi:10.1145/1449814.1449865

Collaboration and communication

2008· article· en· W2084739116 on OpenAlexaff
Steven Fraser, Ricardo D. Lopez, Pradeep Kathail, Doug Schmidt, Mary Shaw, Kevin Sullivan, Dave Thomas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsSoftware deploymentComputer scienceRobustness (evolution)Intellectual propertyFunction (biology)Risk analysis (engineering)Knowledge managementProcess managementBusinessSoftware engineering

Abstract

fetched live from OpenAlex

Mission- and life-critical Ultra-Large-Scale (ULS) systems are increasingly prevalent and networked in many domains, including business, aviation, communication, defense, finance, health, and public utilities. Such systems are often too complex for generally centralized methods to work well for such tasks as requirements discovery, development, system integration, test, deployment, configuration, operation protection, and evolution. Yet today we lack sound methods and technologies for distributing these tasks across large ecosystems of system production. What technical, legal, contractual, and cultural frameworks are needed to enable global partners with independent, sometimes conflicting agendas, to function effectively in the execution of such tasks? Can a competitive and collaborative distributed design ecosystem deliver value and robustness over time consistent with demands for quality, intellectual property protection, and other such requirements? Join this panel of industry experts and academic researchers who will share and debate their perspectives and lay out a vision for the future.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.067

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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 designTheoretical or conceptual
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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering Techniques and PracticesFrench-language works237,207