ELECTRONIC DISCLOSURE AND FINANCIAL KNOWLEDGE MANAGEMENT
Bibliographic record
Abstract
In this paper, we reported the benefits of using eXtended Markup Language (XML) to support financial knowledge management and discussed number of issues associated with developing an XML-based financial knowledge management system. Current searching engines do not provide sufficient performance in terms of recall, precision, and extensibility for financial knowledge management, because the data represented in HTML format cannot support financial knowledge management effectively. On the other hand, XML provides a vendor-neutral approach to structure and organize contents as XML authors are allowed to create arbitrary tags to describe the format or structure of data. A prototype of XML-based ELectronic Financial Filing System (ELFFS-XML) is developed, and value-added services such as automatic tag generation and cross-linking related information from different data sources are provided to enable knowledge representation and knowledge generation. We compared the XML-based ELFFS with the original HTML-based ELFFS and SEDAR - an electronic filing system used in Canada, and we found that ELFFS-XML is able to provide much more functionalities to support knowledge management. We also compared our automatic tag generation result with the experts' and investors' choices, and recommended some directions for future development of similar electronic filing systems.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".