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Record W3122291294

Massively Distributed Authorship of Academic Papers

2012· article· en· W3122291294 on OpenAlexaff
Bill Tomlinson, Joel Ross, Paúl André, Eric P. S. Baumer, Donald J. Patterson, Joseph Corneli, Martin Mahaux, Syavash Nobarany, Nithya Sambasivan, Marco Lazzari, Birgit Penzenstadler, Andrew W. Torrance, David Callele, Gary M. Olson, M. Six Silberman, Marcus Ständer, Fabio Romancini Palamedi, Albert Ali Salah, Eric Morrill, Xavier Franch, Florian Mueller, Joseph Kaye, Rebecca W. Black, Marisa Leavitt Cohn, Patrick C. Shih, Johanna Brewer, Nitesh Goyal, Pirjo Näkki, Jeff Huang, Nilufar Baghaei, Craig Saper

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsScholarshipCrowdsourcingMassively parallelComputer scienceProcess (computing)NegotiationExperiential learningData scienceKey (lock)Work (physics)Scale (ratio)World Wide WebSociologyPolitical scienceEngineeringComputer securitySocial sciencePedagogy
DOInot available

Abstract

fetched live from OpenAlex

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

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)0.010
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.119
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.013
Science and technology studies0.0030.002
Scholarly communication0.0100.010
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.011

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 head0.334
Teacher spread0.315 · 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.

Study designObservational
DomainEvaluation
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

Citations1
Published2012
Admission routes1
Has abstractyes

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