MétaCan
Menu
Back to cohort
Record W1946135105 · doi:10.1002/cpe.3420

P2Pedia: a peer‐to‐peer wiki for decentralized collaboration

2014· article· en· W1946135105 on OpenAlexaff
Alan Davoust, Alexander Craig, Babak Esfandiari, Vincent Kazmierski

Bibliographic record

VenueConcurrency and Computation Practice and Experience · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceSynchronizingWorld Wide WebPeer-to-peerSelection (genetic algorithm)Process (computing)Collaborative editingSocial network (sociolinguistics)File sharingInformation retrievalSocial mediaThe Internet

Abstract

fetched live from OpenAlex

Summary Existing Wiki systems such as Wikipedia depend on a centralized authority and cannot easily accommodate multiple points of view. We present P2Pedia, a social peer‐to‐peer wiki system, where users have their own local repository and can collaborate by creating, discovering, editing, and sharing pages with their peers but without synchronizing them. Multiple versions of each page can thus co‐exist on each repository and across the network, which allows for multiple points of view. Browsing or searching the wiki thus yields multiple page versions; to help the user's page selection process, the system annotates search results with trust indicators based on the distribution of each version in the peer repositories and the topology of the social network. We describe an experimental study where the system was deployed for academic writing exercises, and we analyze the results to validate different aspects of this collaboration principle. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.005
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.027
GPT teacher head0.427
Teacher spread0.399 · 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 designNot applicable
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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueConcurrency and Computation Practice and ExperienceSame topicWikis in Education and CollaborationFrench-language works237,207