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Record W2077110404 · doi:10.1145/2215676.2215680

Report on BooksOnline'11

2012· article· en· W2077110404 on OpenAlexfundno aff
Gabriella Kazai, Carsten Eickhoff, Peter Brusilovsky

Bibliographic record

VenueACM SIGIR Forum · 2012
Typearticle
Languageen
FieldComputer Science
TopicWeb Data Mining and Analysis
Canadian institutionsnot available
FundersCity, University of LondonConnaught FundUniversity of TorontoUniversity of Twente
KeywordsComputer scienceCrowdsourcingSalientSocial mediaReading (process)Context (archaeology)Digital libraryWorld Wide WebKey (lock)Library scienceData sciencePolitical scienceHistory

Abstract

fetched live from OpenAlex

The BooksOnline Workshop series aims to foster the discussion and exchange of research ideas and initiatives addressing challenges and exploring opportunities around large collections of digital or digitized books and complementary media. The fourth workshop in the series, BooksOnline'111 called for special attention to the role of social media and the phenomena of crowdsourcing in the context of online books, which are expected to be key in defining new user experiences in digital libraries and on the Web. The workshop boasted a high quality program, including keynote addresses by Ville Miettinnen, CEO of Microtask and Adam Farquhar, Head of Digital Library Technology at The British Library. From the accepted papers, two main themes became salient: 1) The role of relationships among authors, communities and books, and 2) Reading experiences and behaviours. This paper provides a summary of the workshop, its accepted contributions and the subsequent plenary discussion.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.561
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0050.001
Scholarly communication0.0180.007
Open science0.0040.009
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.5610.427

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 head0.025
GPT teacher head0.279
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2012
Admission routes1
Has abstractyes

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