Bibliographic record
Abstract
On October 15th, 2020, the Canadian Association for Security and Intelligence Studies (CASIS) Vancouver hosted its sixth Digital Roundtable event of the year, Intrastate Warfare. The presentation was conducted by guest speaker Dr. Arjun Chowdhury, Associate Professor of Political Science at the University of British Columbia. Dr. Chowdhury’s presentation delivered a historical overview of types of conflicts, and a brief analysis on the patterns of conflicts and whether they have changed over a period of approximately 200 years, with a particular focus on the last 50 years. He described two types of war, interstate and intrastate, mentioning trends in intrastate war and the contrast to interstate war, as well as the consequences to life expectancy and infrastructure in the regions affected by intrastate wars. Subsequently, Dr. Chowdhury answered questions submitted by the attendees, which elaborated on the concepts of interstate and intrastate wars, using current examples such as, COVID-19, right-wing extremism, cybercrimes, and foreign aid. APA Citation CASIS Vancouver. (2020). Intrastate warfare. The Journal of Intelligence, Conflict, and Warfare, 3(2), 66-71. https://journals.lib.sfu.ca/index.php/jicw/article/view/2411/1814.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.108 | 0.029 |
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".