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Record W4245535055 · doi:10.32920/ryerson.14645568.v1

Inclusive internet participation in the network society

2021· preprint· en· W4245535055 on OpenAlexfundno aff
D. Sol

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersMcGill University
KeywordsThe InternetPublic relationsNormativeDigital divideSociologyDiversity (politics)Through-the-lens meteringPolitical scienceOnline participationInternet privacyComputer scienceWorld Wide WebEngineeringLens (geology)

Abstract

fetched live from OpenAlex

Websites and Internet applications that allow user interaction and participation in online discourses have captured the attention of planners and researchers for the potential to increase engagement. However, there is concern about how inclusive these initiatives are of cultural diversity. In this paper I look beyond the binary ‘digital divide’ concept of having Internet access or not in an attempt to bridge the gap between the high level of abstraction present in discussions of the ‘network society’ or ‘global cities’ with the normative discussions of online citizen participation in planning practice. A theoretical analysis of what participation by diverse publics online entails and what the stakes are is combined with a discussion of Web 2.0 practices to provide a ‘lens’ for considering the potential of Internet tools to serve diverse communities as the technology and our use of it continues to change. This analysis informs the recommendation that principles of collaborative planning and expressions of local knowledge should guide future research and practice.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.022
Scholarly communication0.0130.019
Open science0.0010.017
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.049
GPT teacher head0.398
Teacher spread0.350 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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