From unstructured HTML to structured XML: how XML supports financial knowledge management on the Internet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reports the benefits of using extensible markup language (XML) to support knowledge management of financial information. Current search engines cannot provide sufficient performance to support users of financial information, which includes both non‐structured items and well‐structured items. For investors, making a high‐quality decision sometimes requires both. XML can help by providing tags to create structure. XML provides a vendor‐neutral approach. XML authors can create arbitrary tags to describe the format or structure of data, and are not restricted to the tags in the specification for HTML. A prototype 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. Compares the functionality of XML‐based ELFFS with the original HTML‐based ELFFS and SEDAR, an electronic filing system used in Canada, and recommends 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it