Bibliographic record
Abstract
Canada is the second largest country in the world, occupying approximately two-fifths of North America.Also, it is one of the most sparsely populated country with highly concentrated metropolitan population.The metropolitan safety is ensured by ten provinces and three territories with overall guidance of federal government.For instance, federal government issues security policies while most of the execution is left with the provincial government.Given that 80% of the Canadian population lives in metropolitan areas, the importance of metropolitan security is evident.In this paper, the concept of metropolitan security is examined in modern issues with the examination of security policies and applications.This article explores the multifaceted aspects of metropolitan safety in Canada, focusing on the challenges posed by irregular migration, public health crises, climate change, and organized crime.It examines the roles of federal and provincial governments in ensuring urban security and highlights the importance of evidencebased policies in addressing these issues. ÖzKanada dünyanın ikinci en büyük ülkesi olarak Kuzey Amerika kıtasının beşte ikisini kapsamaktadır.Bununla beraber, ülke nusufu çok dağınık ve aynı zamanda metropol alanlara sıkışmış bir yapı sergilemektedir.Eyaket sistemiyle yönetilen Kanada'da on eyalet ve üç bölgesel (Territory) hükümet metropol güvenliğini Federal hükümetin öncülüğünde yönetmektedir.Örneğin, Kanada federal hükümeti metropol güvenlik politikalarını belirlerken güvenlik uygulamaları büyük ölçüde eyalet ve metropol kurulumları tarafından yerine getirilmektedir.Kanada nufusunun yaklaşık %80'inin metropollerde yaşadığı göz önünde tutulduğunda metropol ülkede metropol güvenliğinin önemi daha iyi değerlendirilebilir.Bu araştırmada Kanada metropol güvenlik olgusu güncel sorunlar bağlamında ele alınarak metropol gövenlik politika ve uygulamarı tartışılmış ve son olarak önerilere yer verilmiştir.Bu çalışma, Kanada'da metropol güvenliğinin çok yönlü yönlü değerlendirilmelerinde düzensiz göç, halk sağlığı krizleri, iklim değişikliği ve organize suçlar gibi çok boyutlu zorluklara odaklanmaktadır.Ayrıca, Federal ve Eyalet hükümetlerinin kentsel güvenliği sağlamadaki rollerine ve söz konusu sorunların ele alınmasında kanıta dayalı politikaların önemi vurgulanmaktadır.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".