Risk Analysis of Enterprise Management Accounting Based on Big Data Association Rule Algorithm
Bibliographic record
Abstract
Abstract With the popularization of the development and applications of information technology, enterprise’s management in making decisions is paying more attention to “data driven” and depend on the objective data rather than subjective judgement, in addition to the basic financial data, enterprises in daily operation will produce a very large number of data, despite big data itself is a special kind of enterprise assets, but in order to get valuable information from these data, we must filter out the useless data based on the analysis method of big data, mine and sort out the comparable information, and provide reference for decision makers. The combination of big data and management accounting is the inevitable development trend in future, this article adopted the method Empirical Analysis to establish enterprise management accounting risk analysis model and simulated enterprise using big data analysis method, determined ten key financial indicators the enterprise needed to pay attention to in enterprise risk control, to show the application of big data analysis method in the process of enterprise management accounting, and looked forward to the future of interdisciplinary integration of big data and management 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".