Secret Admirers: An Empirical Examination of Information Hiding and Contribution Dynamics in Online Crowdfunding
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
Individuals’ actions in online social contexts are growing increasingly visible and traceable. Many online platforms account for this by providing users with granular control over when and how their identity or actions are made visible to peers. However, little work has sought to understand the effect that a user’s decision to conceal information might have on observing peers, who are likely to refer to that information when deciding on their own actions. We leverage a unique impression-level dataset from one of the world's largest online crowdfunding platforms, where contributors are given the option to conceal their username or contribution amount from public display, with each transaction. We demonstrate that when campaign contributors elect to conceal information, it has a negative influence on subsequent visitors’ likelihood of conversion, as well as on their average contributions, conditional on conversion. Moreover, we argue that social norms are an important driver of information concealment, providing evidence of peer influence in the decision to conceal. We discuss the implications of our results for the provision of online information hiding mechanisms, as well as the design of crowdfunding platforms and electronic markets more generally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 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 it