A Work-Systems Approach to Classifying Risks in Crowdfunding Platforms: An Exploratory Analysis.
Bibliographic record
Abstract
Crowdfunding has attracted much attention in the last few years because it has opened up new pathways for projects to obtain financing from individuals who are non-professional investors via the Internet. While risk occupies a central role in crowdfunding, this notion has been an unexplored area in the information systems literature. To close this gap, we contribute to the literature by identifying the main risks in crowdfunding platforms. Using the Work Systems Risk Framework, we analyze main risks in three equity crowdfunding platforms: Crowdfunder, AngelList and Seedrs. Our findings indicate that operational risk, project management risk, cognitive skill risk, IP risk, quality risk, legal risk and vendor relationship risk factors to be important to crowdfunding platforms. Findings from this study are relevant to platform owners and regulators in assessing the risks of crowdfunding platforms.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".