Factors Influencing User�s Attitude to Secondary Information Sharing and Usage
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
The increasing availability of enormous data about users online, along with availability of sophisticated tools and technology to store, aggregate, and analyze data for secondary use has raised concerns about how to balance the opportunity for secondary use of data with the need to protect the user privacy that may result from harmful use. To develop a privacy protection mechanism that is useful and meets the expectations and needs of the user, it is important to understand user’s attitude to privacy and secondary information sharing and usage of his/her data. While several studies have investigated factors influencing user’s attitude to privacy in primary data collection context, none of the existing studies have provided an understanding of user perception and attitude to privacy in secondary context. To fill this gap, this work has identified five factors that are important in a secondary usage context and carried out a study on their influence on user’s perception with respect to how their data is shared for secondary use. The main contribution of this paper is an understanding of factors influencing user decisions about privacy in secondary context, which can assist both technology designers and policy makers in the development of appropriate privacy protection that meets the needs and expectations of the user.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| 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 it