Bibliographic record
Abstract
Abstract - We are proposing a platform for a new form of the book, the sBook, which is a combination of the codex book and the e-book. The sBook combines the advantages of these two formats of the book as well as some additional features that we have designed for this new hybrid book. We describe one possible form of an sBook, which is a codex book that has been “smart tagged” so that the book directs one’s Enabler to a Web site that contains the digital form of the text of the codex book. The Enabler could be a desktop or notebook computer, a PDA or a smart phone such as the iPhone. As a result the sBook system - consisting of the codex book, the Enabler, and the Web site - is readable, searchable, networkable, updatable, smart and promotes “active reading”. The book is very readable because the sBook still retains the codex format of ink on paper. Because the “smart tag” directs the reader to a Web site with the digital text and room for comments by readers and updates by the author, the sBook is searchable, networkable and updatable. Finally by incorporating a recommender system installed on the Enabler the sBook can match its content to that of the reader’s research and information interests. The recommender system can also search the Net for pertinent information. This paper explores some of the possible applications of the sBook and its impact on authors, readers, publishers, booksellers and libraries.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".