Research on the Cultivation Model of Innovation and Entrepreneurship Ability for Accounting Majors in the Context of Big Data
Bibliographic record
Abstract
"Mass entrepreneurship and innovation" (hereinafter referred to as "mass entrepreneurship and innovation") is an important support for China's economic development. Relying on greater stimulation of market vitality and social creativity, it can withstand the downward pressure of the economy and maintain the long-term fundamentals of China's economy. On the other hand, it is also an important measure to enhance students' employment ability and expand the scope of employment. Enhancing students' awareness and ability of "innovation and entrepreneurship" is an important driving force in supporting the country's "growth and employment stability". Taking this as the starting point, the article first analyzes the problems in innovation and entrepreneurship education in the accounting profession under the current education system. Then, starting from the background of big data, it fully studies the impact of big data on the accounting profession, and analyzes the problems from three aspects: curriculum system construction, talent team construction, and teaching assessment method construction that meet the requirements of "innovation and entrepreneurship", Finally, a reform strategy for innovation and entrepreneurship teaching in accounting majors under the background of big data was proposed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".