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Going Virtual

2009· book-chapter· en· W4236803684 on OpenAlexaff
Evangelia Baralou, Jill Shepherd

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVirtuality (gaming)IBMKnowledge managementInformation and Communications TechnologyBusinessVirtual teamEngineeringComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Virtuality is a socially constructed reality mediated by electronic media (Morse, 1998). Virtuality has overcome the stage of being considered a “false” reality, and is now being recognized as a process of becoming through information and communication technologies (ICTs), one of the main changing trends in a world in which ownership of assets is overrated. Organizations such as Amazon, Google, Cisco Systems, IBM, Intel Capital, Orange, and Hewlett-Packard are some of the innovative enterprises that have adopted virtual teams in order to accelerate access to global business. For example, most of the people working in product development in Orange Group, one of the UK’s leading mobile phone service providers, work in virtual teams. The World Bank is also using virtual teams that collaborate across national and technical boundaries to meet organizational objectives. IBM, in a way to open up the innovation process, is pulling a technology- enabled global team (around 100,000 people) together for the online equivalent of a town meeting (Business Week Online, 2006) that will hopefully lead to idea generation by the whole IBM population, and powerful innovations in IBM. Characterized mainly by the dimension of timespace distantiation (Giddens, 1991) virtuality has an impact on the nature and dynamics of knowledge creation (Thompson, 1995), innovation (MacKenzie, 2006), social identity (Papacharalambous & McCalman, 2000), and organizational culture (available at http://www.etw. org/2003/Archives/telework2001-proc.pdf). The relentless advancement of ICT, in terms both of new technology and the convergence of technology (e.g., multimedia), is making virtual networking the norm rather than the exception. Socially, virtual communities are more dispersed, have different power dynamics, are less hierarchical, tend to be shaped around special interests, and are open to multiple interpretations, when compared to face-to-face equivalents. To successfully manage virtual communities, these differences need first to be understood, second, the understanding related to varying organizational aims, and third, the contextualised understanding needs to be translated into appropriate managerial implications. In business terms, virtuality exists in the form of lifestyle choices (home-working), ways of working (global product development teams), new products (virtual theme parks), and new business models (e.g., Internet dating agencies). Socially, virtuality can take the form of talking to intelligent agents, combining reality and virtuality in surgery (e.g., using 3D imaging before and during an operation), or in policy making (e.g., combining research and engineering reports with real satellite images of a landscape with digital animations of being within that landscape, to aid environmental policy decisions). Defining virtuality today is easy in comparison with defining, understanding, and managing it on an ongoing basis. As the title “Going Virtual” suggests, virtuality is a matter of a phenomenon in the making, as we enter into it during our everyday lives, as the technology develops, and as society changes as a result of virtual existences. The relentless advances in the technical complexity which underlies virtual functionality and the speeding up and broadening of our lives as a consequence of virtuality, make for little time and inclination to reflect upon the exact nature and effect of going virtual. As it pervades the way we live, work, and play at such a fast rate, we rarely have the time to stop and think about the implications of the phenomenon.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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.

Study designTheoretical or conceptual
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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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