The SEC's XBRL Voluntary Filing Program on EDGAR: A Case for Quality Assurance
Bibliographic record
Abstract
XBRL (eXtensible Business Reporting Languag) was developed to provide users with an efficient and effective means of preparing and exchanging business reporting, and especially financial information over the Internet. After years of development, XBRL is now in the implementation stage, with many companies, governments, regulators, and stock exchanges around the world implementing or planning to adopt XBRL for electronic filing of financial reports and other business documents and filings. In this paper, we examine the XBRL-Related Documents furnished to the SEC’s XBRL Voluntary Filing Program VFP on EDGAR from its inception to December 31, 2007, and report findings from our observations and validation tests. We identify persistent and increasing quality control and assurance issues pertaining to the XBRL-Related Documents furnished under the VFP and discuss potential countermeasures needed to ensure that XBRL-Related Documents are reliable and gain user confidence and acceptance.
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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 | 0.087 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".