“Privacy Is a Privilege”: A Thematic Analysis of the Concerns and Advice of Canadians Crowdfunding for Basic Living Needs
Bibliographic record
Abstract
ABSTRACT Crowdfunding campaigners can struggle with protecting their personal information while providing a compelling case for why their campaign is worthy of support. Little research has examined donation‐based crowdfunding campaigners' experience of protecting their sensitive health‐related information while trying to meet their fundraising goals. Our research uses semi‐structured interviews to explore the concerns of donation‐based crowdfunding campaigners who are raising funding for health and/or housing‐related reasons, which can be considered basic‐living needs. Participants were primarily recruited through two methods: identifying potential participants through a database of scraped crowdfunding campaigns and posting calls for participants on Twitter, Craigslist, and Reddit. We interviewed 24 participants from Canada. All participants were above the age of 19 and crowdfunded for their own health and/or housing‐related needs. Thematic analysis was conducted to identify broad domains that encapsulate participants' privacy concerns and the advice they have for others related to privacy protection. Participants identified three areas of privacy‐related concerns arising from their experience of creating a crowdfunding campaign for themselves: (1) the public nature of crowdfunding campaigns and related harms; (2) being messaged or intrusively probed by potential donors; and (3) the information collection and sharing by platforms. Based on their experiences of crowdfunding, participants recommended people considering starting a crowdfunding campaign to be both cautious and specific; be informed; and consider the uncertainties related to the practice of crowdfunding. Our analysis indicates campaigners are often unaware of the significant pressures they will face to give up their privacy in exchange for financial support. Participants' privacy‐related concerns are complex and intertwined. Better intervention from crowdfunding platforms and government policymakers can help better inform and protect these campaigners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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".