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Review of Open Source Software (OSS)

2012· book-chapter· en· W4238762565 on OpenAlex
Bhasker Mukerji, Ramaraj Palanisamy

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

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsPopularityGovernment (linguistics)Developing countryBusinessOpen sourceCashOpen source softwareSoftwareEconomic growthFinanceComputer sciencePolitical scienceEconomics

Abstract

fetched live from OpenAlex

The popularity of Open Source Software (OSS) in developing countries is quiet evident from its widespread adoption across government departments and public sector organizations. The use of OSS saves economic resources of cash starved countries, provides an opportunity to promote e-government, and to utilize their resources in other sectors. Many developing countries have a large pool of skilled developers who can modify the source code of the OSS at a very low cost. Many governments in developing and developed countries have switched to OSS which probably encourages others to follow the trend. It was not possible to follow the adoption trend in all the developing countries but the usage of OSS in countries like India, Brazil, and Venezuela provides us an insight. The successful adoption of OSS requires thorough analysis of its advantages as well as the issues associated with it. This chapter will provide an overview of OSS, characteristics of OSS developers, and their motivation to volunteer by contributing in OSS projects, followed by the advantages and issues associated with 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0050.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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