Electronic Disclosure and Financial Knowledge Management.
Bibliographic record
Abstract
In this paper we report the benefits of using eXtended Markup Language (XML) to support financial knowledge management, which include indexing, organizing, association generation, cross-referencing, and retrieval of financial information to support the generation of knowledge. The current searching engines cannot provide sufficient performance, such as, recall, precision, extensibility, etc, to support users of financial information. XML is able to partially solve such problem by providing tags to create structures. XML provides a vendor-neutral approach to structure and organize contents. XML authors are allowed to create arbitrary tags to describe the format or structure of data, rather than restricted to a specific number of tags given in the specification of HTML. A prototype of XML-based ELectronic Financial Filing System (ELFFS-XML) has been developed to illustrate how to apply XML to model and add value to traditional HTML-based financial information by cross-linking related information from different data sources, which is an important step in moving from traditional information management to knowledge management. We compared the functionality of XML-based ELFFS with the original HTML-based ELFFS and SEDAR, an electronic filing system used in Canada, and recommended some directions for future development of similar electronic filing systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".