Trust and Support for Surveillance Policies in Canadian and American Opinion
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.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".