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Record W3111825163 · doi:10.23962/10539/30356

Social Entrepreneurs' Use of Fab Labs and 3D Printing in South Africa and Kenya

2020· article· en· W3111825163 on OpenAlexfundno aff
Tobias Schonwetter, Bram Van Wiele

Bibliographic record

VenueThe African Journal of Information and Communication (AJIC) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
FundersAmerican University in CairoSocial Sciences and Humanities Research Council of CanadaUniversity of Cape TownDepartment for International DevelopmentInternational Development Research CentreUniversity of JohannesburgUniversity of Ottawa
Keywords3D printing3d printerOrder (exchange)SoftwareBusinessOpen sourceOpen source software3d printedMarketingWorld Wide WebEngineeringComputer scienceManufacturing engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This article outlines findings from a study in South Africa and Kenya that explored social entrepreneurs' use of fabrication laboratories (fab labs), and in particular fab lab 3D printing services, in order to advance their social innovations and enterprises. Based on interviews with representatives of fab lab initiatives and social enterprises, the study found strong linkages between social entrepreneurship and fab labs, and between social entrepreneurs and the use of 3D printing technology. However, it was also found that social entrepreneurs tend not to rely primarily on fab labs for access to 3D printers, preferring to buy and build their own printer units-a practice made cost-effective through the selection of low-cost, open source models. In respect of the computer-aided design (CAD) software used to design the files for 3D printing, it was found that social entrepreneurs prefer the stability and user-friendliness of proprietary CAD software, despite the cost implications. At the same time, it was found that social entrepreneurs frequently use free and open source CAD files available online, and that they seek, in turn, to share their designs on a free and open source basis.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.034
GPT teacher head0.213
Teacher spread0.179 · 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 teacher head, 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

Citations10
Published2020
Admission routes1
Has abstractyes

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