MétaCan
Menu
Back to cohort
Record W1600625831

Development Success in Open Source Software Projects: Exploring the Impact of Copylefted Licenses

2005· article· en· W1600625831 on OpenAlexaff
Jorge Colazo, Yulin Fang, Derrick J. Neufeld

Bibliographic record

VenueJournal of the Association for Information Systems · 2005
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsWestern University
Fundersnot available
KeywordsOpen source softwareProductivityEthosExploratory researchKnowledge managementOpen sourceSoftware developmentProcess (computing)SoftwareOpen-source software developmentSoftware qualityBusinessEngineeringComputer sciencePolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

Copyleft prevents the source code of open source software (OSS) from being privately appropriated. The ethos of the OSS movement suggests that volunteer developers may particularly value and contribute to copylefted projects. Based on social movement theory, we hypothesized that copylefted OSS projects are more likely than non-copylefted OSS projects to succeed in the development process, in terms of two key indicators: developer membership and developer productivity. We performed an exploratory study using data from 62 relevant OSS projects spanning an average of three years of development time. We found that copylefted projects were associated with higher developer membership and productivity. This is the first study to empirically test the relationship between copylefted licenses and OSS project success. Implications for OSS project initiators as well as future research directions are discussed.

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)0.010
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.304
Teacher spread0.256 · 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.

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

Citations12
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Association for Information SystemsSame topicOpen Source Software InnovationsFrench-language works237,207