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Record W1496400259 · doi:10.1109/iemc.1996.547815

Virtual organizations-an opportunity for learning

2002· article· en· W1496400259 on OpenAlexaff
Francis T. Hartman, Rafi Ashrafi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsUniversity of CalgarySocial Sciences and Humanities Research CouncilNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsEntertainmentProcess (computing)Government (linguistics)BusinessProduct (mathematics)Entertainment industryKnowledge managementMarketingComputer science

Abstract

fetched live from OpenAlex

As the idea that new enterprises may be formed and operated successfully without the normal physical constraints and trappings of office buildings, incorporation organization charts and so on takes hold, so other opportunities will arise. One of these is the opportunity to learn and improve through the adoption of better practices from hitherto alien cultures. This paper presents just some of the intriguing notions that arise from the findings of a recent pilot study on project management practices in seven different industries. The industries investigated were product development, utilities, oil and gas, entertainment, infrastructure (traditionally government), systems development and construction. The findings include a number of potential areas for significant process improvement through the adoption of ideas and practices of one industry by another. The opportunities for such improvements will be enhanced through virtual organizations that span different industries and cultures. This is just one more area where competitiveness may be enhanced.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.009
Scholarly communication0.0140.017
Open science0.0010.019
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0190.003

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 head0.032
GPT teacher head0.240
Teacher spread0.209 · 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
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
Published2002
Admission routes1
Has abstractyes

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