CAP Forum on E‐Business: Compromise or Customize: XBRL's Paradoxical Power
Bibliographic record
Abstract
ABSTRACT Business reports are changing in response to regulatory and market demands. Requests by regulators for electronic filings of financial statements and tax forms are increasing and such filings are rapidly becoming mandatory in many countries. In response, extensible business reporting language (XBRL) is a market‐driven, collaborative effort to make electronic filings more useful to, and to reduce the burden on, both publishers and consumers of business reports. XBRL does much more than simply list data items that can be submitted in an electronic filing. XBRL is a complete set of tools for regulators or groups to fully communicate the meanings of and interrelationships among the business reporting concepts. In addition, core sets of concepts from regulators or groups can be extended, expanded, or otherwise modified for more specific communication by jurisdictions, industries, or individual corporations. This unique customization capability lets companies better present their electronic filings as parallels to their paper filings. A “customizable standard” offers new opportunities and new challenges. This paper discusses XBRL's paradoxical power ‐ the trade‐offs between customizing to better parallel existing paper reports and compromising to more closely match the standards, and the research needed for the transition from freeform to customized reports.
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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 | 0.064 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".