Enhancing Scholarly Publications: Developing Hybrid Monographs in the Humanities and Social Sciences
Bibliographic record
Abstract
Enhancing publications has a long history but is gaining acceleration as authors and publishers explore electronic tablets as devices for dissemination and presentation. Enhancement of scholarly publications, in contrast, more often takes place in a Web environment and is coupled with presentation of supplementary materials related to research. The approach to enhancing scholarly publications presented in this article goes a step further and involves the interlinking of the “objects” of a document: datasets, supplementary materials, secondary analyses, and post-publication interventions. This approach connects the user-centricity of Web 2.0 with the Semantic Web. It aims at facilitating long-term content structure through standardized formats intended to improve interoperability between concepts and terms within and across knowledge domains. We explored this conception of enhancement on a small set of books prepared for traditional academic publishers. While the project was primarily an exercise in development, the conclusion section of the article reflects on areas where conceptual and empirical studies could be initiated to complement this new direction in scholarly publishing.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".