Equity and Reward Crowdfunding: A Multiple Signal Analysis
Bibliographic record
Abstract
This paper aims to analyse the success signals of initiatives through equity and reward crowdfunding, the two typologies most used by start-ups and SMEs. This article discusses and compares these two models, highlighting the main differences and similarities, by analyzing the factors that influence the success of initiatives through crowdfunding, measured both in terms of amount of funding raised and number of investors that funded the initiatives. The focus is on three sets of signals, venture quality (human capital, information about the establishment and the status of the initiatives), the level of information the company provides to reduce the degree of uncertainty and campaign quality. Using two distinct datasets, one of 235 equity-model initiatives and one of 274 reward-model initiatives, in both cases analyzing projects that have reached (or exceeded) the funding goal, it turns out that venture quality affects in both types, though distinctly, in particular in the reward model play an important role the awards, in addition to the rounds and the tutors (the latter two also present in the equity model), which constitute the status information of the company, while the information about the establishment and the human capital affects only the equity model. Equally for the equity model affects the level of information to reduce uncertainty, while campaign quality in both types seems to have a very slight impact.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".