The impact of the Internet of Things on the creative accounting practice using big data
Bibliographic record
Abstract
Big data has become more important in practically all businesses throughout the world in the present era of information technology. Big data as a part of the internet of things, creative accounting practices regarding the meaning, methods and motives and the role of big data as a part of the internet of things on the increase of creative accounting practices. The researchers concluded that big data leads to an increase in the percentage of creative accounting practices in the business environment, due to the fact that big data impacts the auditing process and the detection of creative accounting practices such as income smoothing. Despite the fact that Big Data is most commonly used in creative accounting techniques and its relevance cannot be overstated, research and analyses are insufficient. Given the relevance of big data across all industries, this study attempts to undertake a comprehensive literature analysis on the topic of big data and innovative accounting methods across all industries. As a result, the study will add to the body of knowledge by opening up new avenues for empirical research in big data and creative accounting.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".