Colloque S&T Symposium 2009: Understanding the Human Dimension in 21st Century Conflict/Warfare: Taking Care of the Front Line (comprendre la dimension humaine dans les conflits/la conduite de la guerre au xxle siecle: veiller a la ligne de front)
Bibliographic record
Abstract
Abstract : Defence ST Theme 2: Complexity and Conflict; Theme 3: Duty of (to) Care; and Theme 4: Super-Empowered Individuals. As these themes illustrate, the enormous challenges involved in taking care of the front line defy simplistic solutions. Original research presented by representatives from the defence community, academia, and industry illustrate the complexity of the issues involved, and point to a need for inclusive approaches to taking care of the front lines that break down existing barriers between departments within government, military and civilians, the front lines and the home front, and even leaders and their subordinates. What it means to take care of the front line within the future battlespace can only be understood using models that can account for high levels of complexity. To this end, the role of human research within the S&T community is becoming an increasingly important factor that enables the agility and adaptability of the Canadian Forces.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".