Challenges and Potential of Local Loading of XML Ebooks
Bibliographic record
Abstract
This paper is a summary of the experience of local loading of XML Ebooks on the Scholars Portal Ebook platform. It discusses the problems and potential of local loading that emerged from a pilot loading of over 500 titles; the first stage of this pilot was completed in February 2011. More specifically, the paper will review the difficulties encountered during the various stages of the loading, starting from loading files on our MarkLogic server, then the presentation of content via XSLT and ending with transforming the table of contents to achieve functionalities, such as lone-chapter downloading. We will also touch upon our web reader and the features developed to enhance the reading experience of XML Ebooks. Our conclusion is that with the gradual increase in publishers’ switching from PDF to XML format, the need to have a standard for XML Ebooks increases, as well; local loading of XML Ebooks in their current format suggests that much programming work will be called for in order to arrive at the best presentation of the content. Finally, we will suggest that once a satisfying web-based presentation of XML Ebooks is achieved, there will still be an urgent need to develop good readers in order to provide a friendly reading experience.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".