A Futuristic View of Using XBRL Technology in Non-Financial Sustainability Reporting: The Case of the FDIC
Bibliographic record
Abstract
The rapid use and development of information and communication technology capabilities in the public sector has revolutionized the mechanism that government agencies use to collect, process, and disseminate data. Electronic government is one of the strategic initiatives that many government agencies have considered adopting to offer efficient web-based services and operations. Although there have been efforts to examine the implementation process of technological innovations in financial and business reporting, many government agencies are about to face a bigger challenge in developing or adopting current technologies to assess their usefulness for non-financial sustainability reporting. The Extensible Business Reporting Language, XBRL, has been adopted by the U.S. Federal Deposit Insurance Corporation (FDIC) to process financial data in the quarterly call reports filed by banks. Using Rogers’ well-established theory of innovation adoption process, this paper discusses the FDIC’s XBRL implementation process and investigates the roles and experiences of the agency’s stakeholders. A case study research methodology, supported by semi-structured interviews, is used to explore each phase of the implementation process. The findings reveal that the process was facilitated by stakeholder engagement, technical support, and the agency’s strategic decision-making process. This paper contributes to the literature by examining the applications, benefits, and challenges of using XBRL technology to process non-financial sustainability data, which is still an under-researched area. Therefore, the implications for using the technology in non-financial reporting will be insightful for future regulatory adopters and their stakeholders including filer banks, software vendors, and various users of financial and non-financial information.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".