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Record W3122541586

Learning to trust the crowd: Some lessons from Wikipedia

2008· article· en· W3122541586 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.

Bibliographic record

VenueArchipelago (Université du Québec à Montréal) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsEncyclopediaQuality (philosophy)Computer sciencePeer productionControl (management)Task (project management)Process (computing)Value (mathematics)World Wide WebKnowledge managementManagementArtificial intelligenceEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Inspired by the open source software (OSS) movement, Wikipedia has gone further than any OSS project in decentralizing its quality control task. This is seen by many as a fatal flaw. In this short paper, I will try to show that it is rather a shrewd and fertile design choice. First I will describe the precise way in which Wikipedia is more decentralized than OSS projects. Then, I will explain why Wikipedia’s quality control can be and must be decentralized. Next, I will show why it is wise for Wikipedia to welcome anonymous amateurs. Finally, I will argue that concerns about Wikipedia’ quality and sustainable success have to be tempered by the fact that, as disruptive innovations tend to do, Wikipedia is in the process of redefining the pertinent dimensions of quality and value for general encyclopedias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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