Digitization and Financial Reporting – How Technology Innovation May Drive the Shift toward Continuous Accounting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological forces, including but not limited to blockchain and artificial intelligence platforms, are driving change not only in the accounting profession, but business at large. In a business environment where data is produced nearly continuously, and stakeholder groups expect an increasing variety of information, current accounting processes do not appear sufficient. This research examines and applies current market forces, linked to both technology, including an analysis of both blockchain and artificial intelligence, and the increased influence of stakeholders on the reporting process, to do the following. First, an analysis of items to consider and review as the shift toward more continuous accounting and reporting begins is postulated. Second, and arguably more important for the combined practitioner and academic audience this research is intended for, implications and applications of more continuous accounting are put forth to assist as individuals and organizations embrace this transformative process.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it