Political Challenges of Terrorism and the State of Canada: a Case Study of Canada–Iran relations
Bibliographic record
Abstract
The way governments deal with the claims of victims of terrorist incidents is one of the most important political issues of the day. Since the Canadian government claims that it fights terrorism the political strategies that govern this government are important in responding to the survivors of the terrorist events. By accusing the Iranian government, it has done its most important action against one of the victims of terrorism. By using the theory of Barry Buzan in analyzing this action of the Canadian State the findings of this paper show that Canada by utilizing some policies like a particular definition of international terrorism advocating state and regarding Iran as one the biggest countries which advocate terrorism, giving up the diplomatic relation with Iran, repudiating the immunity of Iran’s properties and their seizure in the interests of victims of terrorist attacks in occupies lands is a way of cooperating with the US and Israel. According to Buzan theory about security dilemma and the complexities and contradictions inherent in political choices about that the operation of Canada about the Iranian government's immunity in international law is not defensible. In this article we are looking for answers to this question that "What are the political fields of Canada’s action against Iran?" This review is based on Barry Buzan's theory and was conducted by using descriptive analytical method.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".