Relative Privacy Valuations Under Varying Disclosure Characteristics
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
We investigate changes to the value that individuals place on the online disclosure of their private information in the presence of multiple privacy factors. We capture individuals’ willingness-to-accept for a privacy disclosure in a series of randomized experiments that manipulate characteristics of a required privacy disclosure by altering the information context, the intended secondary use of the disclosed private information, and the requirement to disclose personally identifying information. We collect data from two populations (college students and Amazon Mechanical Turk workers) to aid with generalizability of our results. Across the experiments, we consistently observe null effects for each of the privacy factors. The results provide a unique perspective on privacy valuations by showing that results from prior research on simple privacy decisions may not translate to more realistic, complex privacy disclosure decisions that involve multiple factors. Our findings suggest that disclosing private information may be an all or nothing type of decision as opposed to an activation of individual factors proposed by prior literature as important in a multidimension private information disclosure. This study provides managerial insight into the possible evolution of online disclosure decisions, especially in settings that incorporate multiple disclosure dimensions.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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