Firm-Specific Characteristics of the Participants in the SEC's XBRL Voluntary Filing Program
Bibliographic record
Abstract
ABSTRACT A number of papers have attempted to study firm-specific characteristics of the participants in the SEC-administered XBRL Voluntary Filing Program (VFP). However, to date, their findings have been conflicting—contrary to the underlying theory or inconclusive due to methodological limitations. Some of these limitations include the use of limited subsets of VFP data, the use of portfolio matching designs containing matching weaknesses, and omission of key explanatory variables. This paper attempts to overcome some of these limitations by using a more comprehensive sample, employing a more effective matching procedure, and a more complete set of variables suggested by both voluntary disclosure and organization theories. Consistent with the theory, higher voluntary disclosure propensity, stronger corporate governance, and better profitability are found to be robustly significant factors associated with voluntary XBRL adoption in the U.S. Innovativeness is a distinguishing characteristic for non-high-tech VFP participants. Analyst following, auditor quality, and earnings quality are less robust characteristics.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".