The Digital Intelligent Accounting Talent Training Model and Government-Industry-Academia Collaborative Education: A Perspective from Triple Helix Theory
Bibliographic record
Abstract
The continuous progress of artificial intelligence technology in China has impacted the original pattern of many traditional industries, and the long-established accounting industry is also facing the dilemma of being replaced. In this paper, from the perspective of triple helix theory, on the basis of elaborating the meaning of digital intelligence and the inner mechanism of action, and with the cultivation of composite digital intelligence accounting talents with data analysis and processing ability, original innovation ability and efficient collaboration ability as the cultivation goal, we focus on the tripartite collaborative education model of government-industry-university, and find that the cultivation of new digital intelligence accounting talents in the era of big data can make use of the tripartite government-industry-university. It is found that the cultivation of new digital intelligent accounting talents in the era of big data can make use of the collaborative education platform jointly constructed by government, industry and university, and the interaction of the three main forces in the platform can accomplish the cultivation goal of accounting talents more efficiently and improve the cultivation ability. This paper hopes to provide useful reference for solving the real dilemma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".