MétaCan
Menu
Back to cohort
Record W2099535302 · doi:10.1142/s0219622004000039

ENABLING TECHNOLOGIES FOR THE CREATION AND RESTRUCTURING PROCESS OF EMERGENT ENTERPRISE ALLIANCES

2004· article· en· W2099535302 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Information Technology & Decision Making · 2004
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRestructuringAllianceProcess (computing)Function (biology)Computer scienceTask (project management)Order (exchange)Work (physics)Unit (ring theory)Knowledge managementProcess managementBusinessSystems engineeringEngineering

Abstract

fetched live from OpenAlex

In today's world, it is of utterly importance for enterprises to react in a timely and flexible way to upcoming complex market demands. One solution is given by the concept of virtual enterprise and enterprise alliances, respectively. In order to function efficiently and flexibly such enterprises need to be deeply integrated. Based on previous work combining the concepts of virtual enterprises, holonic organizations and multi-agent systems to support such deep integration, the paper discusses in detail how well-suiting partners and contributors for a given (bunch of) task(s) can be found using today's state-of-the-art technologies. Mapping an enterprise alliance onto a multi-agent system is enabled by a methodology equipping each agent with the ability to deal and consider its own goals (goals of the unit it represents) as well as the goals of the unit in which it is integrated (the higher level unit).

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.013
GPT teacher head0.316
Teacher spread0.303 · 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