Bibliographic record
Abstract
"The purpose of this study was to examine the factors that influence an individual’s choice to share personal information online. Specifically, the role of age, gender, personality, overall media exposure, and perceived risks and benefits were examined in relation to a willingness to share personal information that differed in sensitivity (high school grades, medical records, income) and differed in target audience (social media, online store, general public). A total of 202 individuals participated in this survey study. The majority were young (M age = 22.46 years, SD = 5.77), single (83.7%), women (80.7 %), with at least some post-secondary education (90.1%). A series of hierarchical regression analyses were conducted. The results indicated that willingness to share personal information on social media was predicted by having higher scores on the personality traits of extraversion, agreeableness, and negative emotionality. Higher scores on perceived purchase benefits and total media exposure also predicted willingness to share personal information on social media. In terms of willingness to share personal information with an online store, total media exposure was a significant predictor along with higher extraversion and lower conscientiousness scores. Finally, willingness to share personal information with the general public was predicted by overall media exposure. Participants generally believed that there were risks involved in sharing personal information, but these risks were considered to be slight. As well, they only slightly disagreed when asked if the internet could be trusted, and were neutral on whether there were purchase benefits to providing personal information."
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.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".