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Record W2141101549 · doi:10.1080/1043859052000344705

Who is not developing open source software? non-users, users, and developers

2005· article· en· W2141101549 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconomics of Innovation and New Technology · 2005
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsnot available
FundersUniversity of Guelph
KeywordsInvestment (military)Open source softwareBusinessMarketingOpen innovationKnowledge managementPublic relationsSoftwarePolitical scienceComputer science

Abstract

fetched live from OpenAlex

The development of knowledge requires investment, which may be made in terms of financial resources or time. Open source software (OSS) has challenged much of the traditional reasoning by suggesting that individuals behave altruistically and contribute to a public good, despite the opportunity to free-ride. The lion’s share of the existing literature on OSS examines communities, that is, those individuals whom are already part of the OSS community. In contrast, this paper starts from users with the requisite skill to use and develop OSS. This group of skilled individuals could potentially invest into the development of OSS knowledge, but they may or may not do so in actuality. This paper, therefore, explores three issues, which have not been extensively explored in the literature, namely, (1) how frequently a group of skilled people use OSS, (2) reasons for differences among users and non-users in terms of use and attitudes, and (3) how frequently, and why, some users contribute to OSS projects (and thereby become developers). In doing so, we consider the opportunity costs of use and development of OSS, which has been largely neglected in the literature. We find that the individuals have a rather pragmatic attitude to firms and that many are active in both firms and OSS community, which raises many questions for future research about the role and influence of firms on the development and diffusion of OSS.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.030
GPT teacher head0.267
Teacher spread0.237 · 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