Bibliographic record
Abstract
Most studies of privacy assume that people are concerned about their online privacy, but few studies investigate why.Cognitive Science can advance our understanding by documenting the cognitive rules that influence people's judgments about privacy -judgments about what kind of personal information to reveal to whom.The purpose of my dissertation was to explicate these cognitive rules.Experiment 1 examined if the willingness to consent to share personal information varied with the kinds of personal information requested and the kinds of requestors.Fiftyfour undergraduate students and 12 middle-aged adults rated their willingness to consent to the collection of 12 different kinds of personal information by five different kinds of organizations.Participants also wrote their reasons for consenting/not consenting to share personal information with each kind of organization.Results showed that the willingness to consent varied with the kinds of personal information requested, and the organization requesting the personal information.Reasons for consenting more often reflected self-interest and reasons for not consenting more often reflected moral reasons.Willingness-to-consent ratings were also correlated with personality variables.For example, the more participants rated themselves as anxious the less willing they were to consent to share personal information.Experiment 2 explored possible double standards of willingness to consent judgments.The same participants as those in Experiment 1 rated whether or not other people should consent to the collection of the same kinds of personal information by the same kinds of organizations.Results showed that participants mostly made similar judgments about self and others' privacy, but sometimes exhibited double standards.For example, participants who rated themselves as reserved rated that others should be less willing than themselves to consent to reveal personal information. Cognitive Rules and Online Privacy iiiExperiment 3 examined if how willing people were to share personal information influenced judges' impressions of them.A different sample of 51 undergraduate students was asked to form impressions of 12 anonymous participants from Experiment 1 (the targets), selected for their variations in willingness to consent to share personal information.Participants recorded their impressions of these 12 targets on scales related to trust, trustworthiness, honesty, friendliness, and likelihood of hiding information.The targets received less favorable impressions the less willing they were to share personal information.Collectively, the experiments indicated that the cognitive rules for judgments about privacy served functions related to self-interest and morality, and were sensitive to the kinds of personal information requested and the nature of the requestor.Prescriptions of what others should be willing to share mostly mirrored people's own willingness judgments, and the less willing others were to share requested information, the more negative impressions of them were formed.Conceptual and practical implications of the findings are discussed.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".