“People I Don’t Even Know Can See This” – Privacy Approaches by Canadians Crowdfunding for Basic Living Needs
Bibliographic record
Abstract
Background The practice of crowdfunding raises many concerns, including the extent to which crowdfunding challenges the privacy of crowdfunding campaigners and beneficiaries. Crowdfunding platforms regularly remind campaigners that an emotionally compelling campaign description that highlights their reasons for creating a campaign is likely to increase their chances of success. As a result, campaigners experience significant pressure to disclose highly personal information. There is a significant lack of empirical research exploring the ways crowdfunding campaigners approach privacy-related decisions and how privacy-related concerns arise. In this paper, we highlight the complex pressures experienced by crowdfunding campaigners and the privacy-related decisions they make while crowdfunding for themselves. Methods We sought to recruit participants who resided within Canada and had used online donation-based crowdfunding to support their own medical and housing-related needs within a year prior to the interview. In total, there were 24 interviews completed. All authors agreed to thematically analyze how crowdfunding campaigners approached privacy in everyday life and how this approach was challenged or affirmed during the process of crowdfunding. Results Our analysis identified three types of approaches participants took to privacy in their everyday lives and further highlighted how these approaches to privacy functioned in the context of crowdfunding. These approaches were: 1. highly guarded and concerned; 2. middle of the road; and 3. mostly open. Conclusions Our study provides evidence indicating the complexity of decisions campaigners make while experiencing various forms of pressures and tensions that challenge their autonomy. While crowdfunding campaigners in our sample used specific strategies from their everyday lives to mitigate the risk of giving up personal information, these strategies were often inadequate in the context of crowdfunding. This could be attributed to the campaigners’ incomplete understanding of crowdfunding Considering this, it is important that government entities and crowdfunding platforms implement policies that better protect crowdfunding campaigns. For example, by clearly communicating terms and conditions and any options available to protect users privacy. There is likely a need for policy to standardize clearer terms of use for technology platforms generally and specifically, and to enact privacy protections for crowdfunding campaigners and recipients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".