Data Ownership and Privacy: Investigating a Shared Psychological Basis
Bibliographic record
Abstract
People often share their personal data online despite reporting that they should not. They also show surprise and distress when data is used in ways they authorize despite giving consent. But, what underlies this inconsistency in people’s thinking? In the present study, we investigated the proposal that thinking about control over information, or informational autonomy, likely underlies variability in thinking about privacy and data ownership. Namely, we propose that threats to one’s autonomy might account for the aforementioned changes in people’s concern for their data. To test this account, we used a survey-style design to measure how a hypothetical threat to the self, and thereby control, influenced adults’ (N = 51) judgments about the ownership and privacy of their personal data. The threat was police lawfully obtaining their data with a warrant. We found that privacy and ownership judgments significantly increased over time. We also found that the variability in participants’ ownership and privacy judgments was related. Together, our findings suggest that privacy and ownership likely have a shared psychological basis, and this shared psychology can likely explain the variability in people’s judgments about personal data across time.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".