MétaCan
Menu
Back to cohort
Record W4386459760 · doi:10.54337/jbm.v11i2.7513

Business Models for Open Source Hardware Repositories

2023· article· en· W4386459760 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Business Models · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsWestern University
Fundersnot available
KeywordsVettingRevenueBusiness modelLeverage (statistics)Computer scienceBusiness caseOpen source hardwareSustainabilityOpen sourceSoftware engineeringProcess managementBusinessSoftwareFinanceOperating systemMarketingComputer security

Abstract

fetched live from OpenAlex

Free and open source hardware repositories provide massive public good, but funding their operation has proven tenuous with conventional business models. This study evaluates business models to foster that public good. Business models for online design repositories are reviewed and a new model is conceptualized to fund repository operations. The greatest added value an open hardware repository brings to the user-developer community is validation and vetting of the designs. A business model was proposed that uses revenue from the vetting process to fund validation studies and sustainable operations of the open hardware repository itself. As the return on investment of laterally-scaled open hardware that can leverage distributed manufacturing has the potential for creating enormous value, maintaining repositories for this hardware enables vast wealth generation for everyone. This is the first study specifically focused on ways to ensure economic sustainability of open hardware repositories.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0010.008
Open science0.0030.001
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 head0.070
GPT teacher head0.298
Teacher spread0.228 · 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