Trust and Support for Surveillance Policies in Canadian and American Opinion
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
After September 11, much legislation has been passed that has impacted negatively upon the tradition of limited government and entrenched privacy rights. Scholarly interest is attracted to the mechanism by which regimes of control and surveillance have disestablished rights without engendering substantial popular resistance. In this article, we analyze a survey of Americans and Canadians on their attitudes toward surveillance and security-based legislation. We develop an argument that trust in government (TGB) produces a tolerance for legislation that limits citizen’s rights. We evaluate our model for both Canada and the United States, given the scholarly debate that these countries differ regionally in their level of TGB and support of statism. We posit that support for surveillance and security legislation is related to respondents’ trust of government, airport officials, and low tolerance of minorities (LTMs). Results suggest that TGB and airport officials as well as LTMs are the key predictors of surveillance and security legislation in both Canada and the United States. Although Quebeckers are more supportive and residents of the U.S. South are less supportive of security and surveillance legislation than the rest of North America, much of the difference in support for such policies can be accounted for by the level of public TGB.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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