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Record W4253579069 · doi:10.1002/spip.401

Does distance still matter?

2008· article· en· W4253579069 on OpenAlexafffund
Timo Wolf, Thanh Nguyen, Daniela Damian

Bibliographic record

VenueSoftware Process Improvement and Practice · 2008
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaInternational Business Machines Corporation
KeywordsIBMComputer scienceJazzTask (project management)SoftwareSoftware developmentData scienceSoftware engineeringProcess managementSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Nowadays, distributed development is common in software development. Besides many advantages, research in the last decade has consistently found that distribution has a negative impact on collaboration in general, and communication delay and time to complete tasks in particular. Adapted processes, practices, and tools are demanded to overcome these challenges. We report on an empirical study of communication structures and delay in IBM's distributed development project Jazz. The Jazz project explicitly focuses on distributed collaboration and has adapted processes and tools to overcome known challenges. We explore the effect of distance on communication and task completion time and use social network analysis to obtain insights about the collaboration in the Jazz project. We discuss our findings in the light of existing literature on distributed collaboration and delays. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.008
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.105
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.005
Scholarly communication0.0070.011
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.002

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 head0.271
Teacher spread0.258 · 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 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

Citations26
Published2008
Admission routes2
Has abstractyes

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