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Record W1591148526 · doi:10.1002/asi.23004

What influences online deliberation? A wikipedia study

2014· article· en· W1591148526 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeliberationUnanimityCredibilityQuality (philosophy)DemocracyPolitical scienceOnline discussionPublic relationsComputer scienceEpistemologyLawPolitics

Abstract

fetched live from OpenAlex

In this paper we describe a study aimed at evaluating and improving the quality of online deliberation. We consider the rationales used by participants in deletion discussions on Wikipedia in terms of the literature on democratic and online deliberation and collaborative information quality. Our findings suggest that most participants in these discussions were concerned with the notability and credibility of the topics presented for deletion, and that most presented rationales rooted in established site policies. We found that factors like article topic and unanimity (or lack thereof) were among the factors that tended to affect the outcome of the debate. Our results also suggested that the blackout of the site in response to the proposed Stop Online Piracy Act ( SOPA ) law affected the decisions of deletion debates that occurred close to the event. We conclude by suggesting implications of this study for broader considerations of online information quality and democratic deliberation.

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.

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)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.334
Teacher spread0.324 · 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