Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.561 | 0.427 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".