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Record W4254217112 · doi:10.15353/joci.v9i3.3151

Introduction

2013· article· en· W4254217112 on OpenAlexvenueno aff
Susana Finquelievich, Mariana Salgado

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyComputer scienceWorld Wide WebPublic relationsKnowledge managementData sciencePolitical science

Abstract

fetched live from OpenAlex

Launching our Call for papers on the role of users in socio-technical innovation has been similar to throwing a bottle with a message into the sea. Who would find it, and how many researchers, in the vast shores of social studies on Information Society, would answer it? What would be the “catch”, in terms of research results, of understanding about the routes in which individuals and groups appropriate and turn information and communication technology useful for their own specific practices? The Call for Papers itself turned into a kind of practical research on how users are relevant regarding socio-technical innovation. A number of colleagues have answered this Call, proving the deep interest which exists in the Community Informatics world about the analysis of the processes through which specialists observe the innovations carried on by communities or individuals and integrate them into new products. In the last decades studies and experience have shown that users matter in regards to technological innovation. Books such as "The Co-Construction of Users and Technology[1]” analyse the creative capacity of users to shape technology in all phases, from design to implementation. Lately, citizen´s labs are also trying to integrate individuals and communities to technological innovation. They try to combine the old “collaboratory” concept launched in the 1990s in academic environments, or virtual laboratories, where scientists collaborate though networking, with the concept of citizens´ networks, in which citizens collaborate in a digital environment for various uses, and that have become freshly popular through social networks such as Facebook or Twitter. Individuals, groups and community have actively participated in the process of technological innovation and are increasingly aware of their capacity for making and changing technologies. Internet - based social networks, open source software, content creation, redesign by use, citizens ´participation in living labs, are just a few examples of people actively enlarging the original uses of information and communication technologies (ICT). The goal of this special issue is to examine, using a variety of multidisciplinary approaches, the mutual interaction between ICT and users. The authors have reflected on the hypothesis that any understanding of users must take into consideration the multiplicity of roles they play, and that the conventional distinction between users and producers is largely formal and artificial. Contributing knowledge about the process in which individuals and communities appropriate and makes information and communication technology functional for their own specific purposes is the goal of this special issue of JOCI. The objective is to advance on the subject of how communities utilize technology, meanwhile creating innovative uses. The papers published in this issue consider how users consume, modify, domesticate, design, reconfigure, and resist technological development, as well as in which ways users are changed by ICT. The papers may be classified into three main categories: Social and Technological Networks, Technological and organizational tools for innovation, Living labs experiences. Some of the key issues that are reflected upon are: - Case studies about technology appropriation and modification of ICT changes by communities. - Alternative-use hunters: analysis of the processes through which experts perceive the changes by communities or individuals and incorporate them into the goods or services. - The follow-up and analysis of the framework of technological relationships between human and non-human agents [1] [1] http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10755

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.591
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0090.008
Open science0.0030.006
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.5910.433

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 head0.026
GPT teacher head0.231
Teacher spread0.205 · 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
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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