Militerisasi Perbatasan antara Negara Amerika Serikat dengan Negara Kanada setelah Serangan Teroris 9/11
Bibliographic record
Abstract
Good relations between the United States and Canada can be said as one of the best relations between countries in the world, this is based on several factors such as similar cultural values, shared political interests, intimate diplomatic relations, and strong economic relationship. At first the border between the United States and Canada was a symbol of good and intimate relations between the two countries so that there was no security at all at the border between the two countries, but this has changed since the 9/11 terrorist attacks. This incident has changed the views of the public and policy makers towards terrorist attacks, this incident has changed national assumptions about terrorism, especially those related to domestic security. After the 9/11 terrorist attacks, migration has become a topic that can be related to terrorism because terrorists can enter a country through immigration gates for residents of a country. The research was made through the collection of qualitative data, as well as through the concept of securitization. This research analyzes the forms of securitization carried out by the United States after the 9/11 incident. The results of this study are that there are many policy changes on the border between the United States of America and Canada after the 9/11 terrorist attacks, this is proven by the Smart Border Action Plan policy which was signed in December 2001 right after the terrorist attack occurred. signifies the urgency to secure the border between the two countries
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".