Bibliographic record
Abstract
Purpose The purpose is to bring together all bibliographic references of the published literature on electronic books (e‐books) and related technologies in one source so that it will save time for others in conducting literature searches and reviewing the developments. Design/methodology/approach The information included in this bibliography is collected systematically from all the published sources in the world such as journal articles, conference papers, conference proceedings, books, reports and PhD theses on e‐books until the last quarter of 2004. Mainly it covers e‐books, e‐books publishing, the impact of e‐books on different types of users, e‐book publishing techniques and trends, e‐book user interfaces and other technologies related to e‐publications. Findings As computer usage continues to grow exponentially, the desire of users to use electronic publications (e‐publications) has also increased tremendously. This has led to the publication of materials in electronic form as e‐publications on both CD‐ROMs and web. The e‐book is one of the several forms of e‐publications and its popularity has been growing steadily for the past decade. Originality/value This bibliography will be useful to all researchers conducting research in any areas related to e‐books and e‐book publishing.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.068 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.025 |
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".