Bibliographic record
Abstract
Canada has embarked on a new approach to security in the post-Cold War era. Through its Minister for Foreign Affairs, Lloyd Axworthy, Canada has championed the concept of human security. This paper analyses Canada's successes and failures with regard to each of the seven components of human security. The opening chapter of this paper analyses human security from the Canadian perspective. The chapter outlines the traditional definition of security that Canada followed during the Cold War and the redefinition that occurred in the post-Cold War era. The chapter then describes how the theory of human security is being put into practice by Canada through peacebuilding initiatives. The second chapter provides a checklist of the seven components that make up human security and Canada's efforts in relation to each component. The seven components of human security that are analysed are economic, food, health, environmental, personal, community, and political. Canada has made positive progress on some of the components of human security. However, for the most part Canada's human security efforts suffer from a severe lack of funding. Canada does not contribute nearly as many financial resources as other like-minded nations and is in serious danger of losing its good international reputation if it continues to shrink its commitments financially.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.035 | 0.044 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".