Crowdfunding Conundrum: Western Ideas and their Chinese Copycats
Bibliographic record
Abstract
In the third quarter of 2021, global crowdfunding investment reached $160 billion. Crowdfunding is a relatively recently emerged phenomenon to raise funding from individuals for nascent business ideas. It is considered as an alternative source of funding for start-ups but copycats of crowdfunding ideas are a serious threat to these start-ups (Hossain and Creek, 2021). Fundraising campaigns on crowdfunding platforms, such as Indiegogo and Kickstarter reach millions of people including fraudsters. Start-ups need to signal quality to increase the funding success in their crowdfunding campaigns (Mollick, 2014). Hence, they display the products along with their operating mechanisms and functions in the crowdfunding campaigns to signal quality to convince the potential funders. However, such detailed display gives fraudsters opportunities to copycat. For example, Chinese factories and designers look for the next promising products to turn into copycats. They can assess the strengths and weaknesses of crowdfunding start-ups and glean information to ascertain how quickly they can enter the market. Longer production times or delivery delays can likewise signal that the entrepreneurs are novice and will likely to have limited resources to protect their ideas or legally stop copycats. Many such products are not patented but it is difficult to protect even patented products as the patent may not give global coverage or even if it gives, it is difficult for start-ups to fight the fraudsters who copy the products. Moreover, most start-ups are not well familiar with the sources of raw materials, manufacturing challenges, and distribution channels at the time of crowdfunding. Their philosophy is to explore these things after securing the funding. Fraudsters instantly steal ideas from crowdfunding platforms, quickly come up with copycats and reach the market well ahead of original start-ups. Thus, fraudsters ruin the business potential of start-ups. How to protect product ideas that are used for crowdfunding is an important question.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; both teacher heads agree on what is shown here.
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".