Bibliographic record
Abstract
Following 9/11, Canadian and American governments took steps to protect themselves from future acts of terrorism. The introduction of anti-terrorism legislation in the form of the Patriot Act in the US and the Anti-Terrorism Act in Canada strengthens connections among federal, state and municipal police enforcement. The new legislation increases police powers, whereby police departments are now subject to implementing terrorism strategies in addition to their regular duties. This study questions how Canadian and American police departments employ new terrorism laws and policies. Utilizing a qualitative approach, twelve departments located in states in the mid west, west and southwest and the province of Ontario took part in interviews for this study. Eight American and 4 Canadian police chiefs and managers provide insight into police response to new anti-terrorism laws and policy. The study is framed around a conceptual analysis examining policy instruments employed by government in the form of mandates and inducements. Capacity building includes the actions of government investing in the implementation of policy. Implementation of anti-terrorism and counter-terrorism policy by both Canadian and American federal governments promotes interagency connectedness in the promotion of proactive and reactive response measures. Intelligence gathering and preparation for disaster response is influenced by availability of personnel, funding and education. The importance placed on counter- and anti-terrorism policy by police is influenced by the size of the department, national differences, geographical position and funding. How police interact with their communities influences government laws and policies for implementation of counter- and anti-terrorism responses at the department level. Relationships between police and community shape how laws and policy are put into practice at the street level.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".