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Record W2302815773

Open source software - Is it real treatment for public sector's software needs?

2005· article· en· W2302815773 on OpenAlexaboutno aff
Gábor László

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsPublic sectorSoftwareInformation and Communications TechnologyComputer scienceUsabilitySoftware developmentPublic relationsSoftware engineeringKnowledge managementData scienceManagement scienceEngineeringWorld Wide WebPolitical scienceEconomicsEconomy
DOInot available

Abstract

fetched live from OpenAlex

Open source software history takes as long as computer program history. Programmers at the beginning of computing used to programming for free software model and history now just reached back to (the root) community model. One can say: “There is no new under the Sun.” But the raised question is about the suitability and usability of these software products for public sector. There are advantages as well as disadvantages… “The coins always have more than one side.” There are many initiatives for using OSS in governmental work, but the real success has not come out yet. The research tries to set-up a theory and quantifies the advantages of OSS against the proprietary software (based on social, technical and economic approaches) thereby contribute to the academia and practice. The difficulty of the research is its complexity. None of above mentioned factors can be examined alone. There is close correlation between these factors. At this stage in this research have the main goals to focus on political and economical influences of OSS and to make a theoretical model for an economically reasonable decision for public sector. This paper examines the reasons why was raised the question about using free software in public sector and try to answer the question why are suitable if suitable these software for public sector. 1 BACKGROUND OF PUBLIC SECTOR’S SOFTWARE NEEDS Industrial nations are undergoing dramatic economic and social transformation. This transformation is characterized by the rapid growing amount of information, wide-ranging implications of Information Communication Technology (ICT) and their impact on the whole economy and the growth of global competition. The societies are in transition to the “Knowledge Society”. ICTs changed the World and the societies. ICTs can provide higher life standard for people. Opposite the higher standard there is the digital divide. The ‘digital divide’ is an umbrella term. Commonly understand the gap between ICT ‘haves’ and ‘have-nots’. Generally, it has two main approaches. One focuses mainly on actual connectivity – infrastructure and access. Worldwide, the gap between those who have access to the Internet and those who do not is enormous. According to the Neilsen/Netratings study as of the first quarter of 2001, only 6% of the World’s population had access to the Internet. Of that 6%, 41% comprised of people living in the United States (US) and Canada. 2 Another approach beyond connectivity is the ICT literacy and skills linked to access that does not mean only access to infrastructure, but it has financial, cognitive institutional and political and social cohesion aspects. In reality, many divides exist: both the internal country divides, as well as divides across countries. Today, governments, business, international and nongovernmental organizations have numerous initiatives to eliminate the ICT-related inequities, not just the result of economic differences in access to technologies, but also in cultural capacity and political will to apply these technologies for development impact. The economy can not eliminate the gap alone, so “intervention” of governments is needed. Around the world number of programs exist to manage this problem. National intellectual capital and innovation are based on human resources that is why so important to strengthen the equality by the governments. Government has special obligations as to protect the integrity, confidentiality and accessibility of public information, to protect the privacy of its citizens, to educate the “next generation”, to creation job and careful management of budget. Government’s functions and operations can only handle by using software applications. The software that is used by governments and controls, handles, transmits the citizens’ personal data have to be transparent to protect citizens rights to privacy. The reason why this paper examines the governemnts role is the size of Public Authorities that are quite different around in the each country from a small villages to a big city. Similar patterns, but different needs. The size determines the role and needs of different PA’s. They also have common difficulties and dilemmas (e.g. they have to change data with other authorities and the central government) and different local “problem”. The solutions differ for each PA’s depend on their demand. Insted of making solution of islands it’s time to thinking holistic. Top-down planning for bottom-up problem that means the government has to make a common guidlene and a common interface. The solutions to new challenges require new approaches just as knowledge mapping and knowledge management within the governmental work. 1.1 Roles of the Governments The Digital Economy transforms governments and also the role of the governments mainly in those areas where the economy is most affected by changes of ICTs, and where the markets would not meet the requirements of social and economical stability. Governments play important roles in creating the proper environment for ICT development, and also have a significant leading role as users of these technologies by creating new modes of behavior in the public at large.

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)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.007
Scholarly communication0.0110.021
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0210.006

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 head0.089
GPT teacher head0.340
Teacher spread0.250 · 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 designNot applicable
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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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