Bibliographic record
Abstract
The combination of formal language theory with the research on structure properties of hypermedia can provide a new framework to investigate the dynamic structure properties of hypermedia. The hypermedia component corresponds to the formal symbol in language, links between the components of hypermedia correspond to the rules of constructing words, and thus the syntax, which describes the constructing rules of words, corresponds to the constituting mechanism of hypermedia structure. Hypermedia can be modeled as a transformation device, and the link following operation is transformed into a sequence of matched pairs(units that could be connected). The set of all link following is shown to be a regular set and the set of all possible outcomes of link following is described by a context free grammar. Hence it provides the basis for the structal computing. The system constructs the virtual document based on the computing result of link following operation and thus generates the adaptive view to the user. Context free grammar used to model the construction of virtual document in hypermedia supports the automatic generation of hypermedia structure grammar, thus providing a novice approach for structural analysis. The method proposed brings into correspondence the construction of virtual documents in hypermedia with the generation of words in context free language and thus constitutes the theory foundation of the further research on browsing semantics and other dynamic characteristics of hypermedia.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".