Remarkable Funders: How Early-Late Backers and Mentors Affect Reward-Based Crowdfunding Campaigns
Bibliographic record
Abstract
In recent years, the growth of the reward-based crowdfunding model has offered new opportunities to meet the funding requirements of start-ups. However, the achievement of the funding target is still a complex goal, given the existence of several information barriers between insiders (project proponents) and outsiders (backers) of a crowdfunding campaign. Within the framework of the information asymmetry and the theory of signals, this study aims to analyse the role of two kinds of previously un-investigated funders in determining the success of crowdfunding campaigns: early-late backers (who are placed at the beginning of the tail of the campaign) and mentors (who are represented by firms acting as more expert backers). The findings indicate that both types of funders are remarkably important for the success of a crowdfunding campaign.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".