Are XBRL Files Being Accessed? Evidence from the SEC EDGAR Log File Dataset
Bibliographic record
Abstract
ABSTRACT We provide evidence of whether users of financial reports are accessing XBRL files, the XBRL component of an SEC filing. The possibility of exempting small companies from the XBRL mandate was raised in a legislative debate in which some argued that XBRL files are not being used by small company investors. Using data from the EDGAR log file dataset, we counted the exact number of user accesses to the XBRL files and their corresponding conventional files in HTML, PDF, or text when users access financial disclosures for SEC filings. During the sample period of the third quarter of 2012 through the first quarter of 2015, we obtained 12,483,699 valid user accesses to 5,016 unique XBRL filings made by 880 small companies that are subject to the legislation. Among the user accesses, 61 percent are to access XBRL files, while 39 percent are to access the conventional (non-XBRL) files. The results suggest that small company investors not only access XBRL files but also prefer them to the non-XBRL files when both are available to download for a filing. Our direct measure of user access provides evidence of possible use of XBRL files by investors. Data Availability: Data are derived from publicly available sources. Contact the first author for the derived dataset.
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 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 | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".