Bibliographic record
Abstract
Failed or fragile states are those that are unable or unwilling to provide a socio-political framework for citizens and meet their basic needs. They are a source of terrorism and international crime, as well as incubators of infectious disease, environmental degradation, and unregulated mass migration. Canada's engagement with countries such as the Congo, East Timor, Bosnia, and Afghanistan underlines the commitment of successive Canadian governments to addressing the threats posed to Western security by state fragility. From Kinshasa to Kandahar: Canada and Fragile States in Historical Perspective brings together leading Canadian historians and political scientists to explore Canada's historic relationship with fragile states. The collection spans the period from the 1960s to the present and covers a geographical range that stretches from the Middle East to Latin America to Southeast Asia. Authors embrace a variety of approaches and methodologies, including traditional archival historical research, postmodern textual analysis, oral history, and administrative studies to chronicle and explain Canada's engagement with fragile and failed states. This collection reflects the growing public interest in the issue of failed states, which are of increasing concern to Canadian policymakers and are making headlines on the world stage. It helps explain the historic forces that have shaped Canadian policy towards failed and fragile states, and provides a platform for a national discussion about Canada's future role addressing state fragility.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.886 | 0.807 |
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; the direct Gemma label and the distilled Codex classifier 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".