Bibliographic record
Abstract
Purpose The purpose of this paper is to describe a novel XML schema language called DTD Schema that solves major limitations of document type definition (DTD) and supports features that XML Schema supports in a simple and concise way. Design/methodology/approach DTD Schema is designed based on DTD and data definition language of object‐oriented and object‐relational databases. It extends DTD with namespaces, richer built‐in types and user‐defined subtypes, local elements and attributes, complex types with nonmonotonic multiple element and attribute inheritance with overriding, blocking, conflict handling, and polymorphism. Findings XML Schema is recommended by W3C as the schema language for XML. It uses a set of predefined XML tags to define the schema, which is often a long, intricate specification, full of details and concepts and its verbose syntax often doubles or triples the document length. It is so complicated that even XML experts do not find it human‐readable, mostly due to the XML‐based syntax. Research limitations/implications The only limitation is that DTD Schema is not in XML. But for the same reason, it is simple and concise. Practical implications DTD schema is halfway between DTD and XML Schema and thus it is less complex and much easier for human to use than XML Schema. Originality/value DTD Schema supports all functionalities of XML Schema and also the best of object‐oriented features including multiple inheritance, overriding, blocking, conflict handling and polymorphism. Therefore, it is much more expressive than XML Schema.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".