Crowdfunding Industry Development: Gaining Leading Role in Digital Economy and Future Trends
Bibliographic record
Abstract
In the course of the study, it was found that crowdfunding companies follow a certain stage in their functioning and development: choosing a platform; category selection; the wording of the crowdfunding campaign; definition of a financial goal; determination of the project duration; definition of a reward system; creating a video message; description of the project; regular updates; monitoring the fundraising process. By conducting research on the international market, it was found that with a market size of $ 358.28 billion and a share of 85.99%, China is the largest crowdfunding market in the world. Also, it should be noted the Asia-Pacific region, as Australia took 4th place with funding of $ 1.49 billion, and South Korea - 5th place with $ 1.13 billion, we also find other countries in the Asia-Pacific region in the list of the largest crowdfunding countries in the world. The results of our study concluded that there are many players in crowdfunding in Europe, especially in the field of P2P lending, compared with the Americas and the Asia-Pacific region, as a whole it is a small region. The article also notes the advantages and disadvantages of the crowdfunding development in modern conditions of market functioning.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".