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Networking Serendipitous Social Encounters in Urban Neighbourhoods

2010· book-chapter· en· W1595928494 on OpenAlexfundno aff
Marcus Foth

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
FundersAustralian Research CouncilCanadian Immunization Research NetworkUniversity of Wollongong
KeywordsAppropriationThe InternetConceptual frameworkUrban planningSociologyEnvironmental planningPublic relationsGeographyEngineeringPolitical scienceCivil engineeringWorld Wide WebComputer scienceSocial science

Abstract

fetched live from OpenAlex

In Australian urban residential environments and other developed countries, Internet access is on the verge of becoming a ubiquitous utility, like water or electricity. From an urban informatics perspective, this chapter discusses emerging qualities of social formations of urban residents that are based on networked individualism and the potential of Internet-based systems to support them. It proposes that appropriate opportunities and instruments that are needed to encourage and support local interaction in urban neighbourhoods. The chapter challenges the view that a mere re-appropriation of applications used to support dispersed online communities is adequate to meet the place and proximity-based design requirements that community networks in urban neighbourhoods pose. It argues that the key factors influencing the successful design and uptake of interactive systems to support social networks in urban neighbourhoods include the swarming social behaviour of urban dwellers, the dynamics of their existing communicative ecology, and the serendipitous, voluntary and place-based nature of interaction between residents on the basis of choice, like-mindedness, mutual interest, and support needs. Drawing on an analysis of these factors, the conceptual design framework of an “urban tribe incubator” is presented.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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