Privacy and Security Concerns Related to Internet Use in Canada
Bibliographic record
Abstract
Internet use in Canada and the us has grown and has transcended the lives of many, as it offers unprecedented :c'onveniences. However , privacy and security concerns related to Internet use are widely prevalent and are -:~considered as barriers to realizing the Internet's fullest ._''.' . ~ ,'. potential in the e-Commerce and e-Health arena. Two related studies were conducted to try and .C''':''~'uriderstand the extent of privacy and security concerns related to Intern~t use. Using a national dataset, study 1 examined the profile of Internet use, as well as Internet privacy and security concerns among Canadians. On average, ;57 percent of respondents used the Internet on a regular basis, and Internet privacy and security concerns were expressed by SO and 78 percent, respectively. Given that Internet privacy and security are complex .constructs, these constructs should be examined from a multidimensional ~erspective. As such, study 2 focused on the development of a tool to measure Internet privacy and security concerns. Another purpose of this study 2'was to examine the relationships between Internet privacy and security factors with e-Commerce (i.e., shopping online) and e-Health (i.e., accessing health information online). This study was examined from the perspective of potential customers in Canada, particularly through a survey of students enrolled at a Canadian university. Internet privacy and security tool development led to five statistically determined factors: Interaction, Data Intrusion, Privacy Policy, Security, and Information/Data Sharing. Further analyses showed that the level of concern was significantly higher in four of five factors for those who did not shop online, compared to those who did. -However, no statistically significant difference emerged in any of the five factors in accessing e-Health information . The results of these two studies may have implications for managerial and government regulatory bodies establishing Internet privacy and security codes with further research based on the present findings. Also, the findings may be used to address customers'/marketers' concerns through evidence-based education programs and the development of alternate marketing strategies among young adults. It is hoped the results will form the basis for future research with other customer groups especially as it relates to the concepts of trust and risk.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".