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Record W2069570166 · doi:10.5555/776816.776954

Addressing the challenges of software industry globalization: the workshop on global software development

2003· article· en· W2069570166 on OpenAlexaff
Daniela Damian, Filippo Lanubile, Heather L. Oppenheimer

Bibliographic record

VenueInternational Conference on Software Engineering · 2003
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGlobalizationWork (physics)Software developmentSoftwareEngineering managementKnowledge managementField (mathematics)Computer scienceEngineering ethicsProcess managementEngineeringPolitical science

Abstract

fetched live from OpenAlex

The goal of this workshop is to provide an opportunity for researchers and industry practitioners to explore both the state-of-the art and the state-of-the-practice in global software development (GSD). Increased globalization of software development creates software engineering challenges due to the impact of temporal, geographical and cultural differences, and requires development of techniques and technologies to address these issues. The workshop will foster interaction between practitioners and researchers and help grow a community of interest in this area. Practitioners experiencing challenges in GSD will share their concerns and successful solutions and learn from research about current investigations. Researchers addressing GSD will gain a better understanding of the key issues facing practitioners and share their work in progress with others in the field.

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)0.023
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0050.007
Scholarly communication0.0140.019
Open science0.0030.016
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0060.002

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 head0.122
GPT teacher head0.332
Teacher spread0.210 · 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

Citations36
Published2003
Admission routes1
Has abstractyes

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