Comparison of Survey Findings from Canada and the USA on Surveillance and Privacy from 2006 and 2012
Bibliographic record
Abstract
This research note highlights the comparative findings of a recent repeat survey of surveillance and privacy. It also draws attention to the usefulness of public opinion surveys for scanning popular responses to surveillance in different contexts and between different countries. The findings from a survey administered in Canada and the USA in 2006, then repeated in a 2012 poll, indicate some continuities and some relevant changes in mood over time. Knowledge of the internet and of softwares such as GPS is relatively high in both countries and this is accented among younger groups, especially males. Similarly, while a higher proportion than previously think they have a say over what happens to their personal data, the younger, the more so. In both countries, more people than before believe that camera surveillance is effective. Curiously, knowledge of laws regulating personal data flows has declined while a greater proportion now consider security-surveillance intrusive. And although responses to workplace surveillance are basically similar, the idea that employers may share data with others is censured. At national borders there is less support for giving extra security checks to visible minorities. People take more steps to protect their personal data in each country, although they worry much more about what corporations, as compared with governments, might do with them. Fluctuations by age and gender occur here too.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".