Lexical sophistication and crowdfunding outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is advised that entrepreneurs should keep the language of venture descriptions short and simple. However, knowledge about the conditions under which language in crowdfunding communications impacts investment behavior is limited. We propose that the use of sophisticated language in crowdfunding venture descriptions creates perceptions of venture distinctiveness that, in turn, increase the amount invested. We test this proposed mechanism across a controlled experiment and present evidence from 886 crowdfunding campaigns that increasing the linguistic sophistication of campaign descriptions can result in a 20% increase in the amount raised by a campaign and greater campaign success. Furthermore, we show that the impact of lexical sophistication on the amount raised is moderated by investor experience whereby experienced investors invest more in ventures with sophisticated language compared to inexperienced investors. Our work contributes to research on communication in crowdfunding by highlighting the significance of lexical sophistication in influencing funding behaviour.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it