Bibliographic record
Abstract
Operations frequently require joint forces to work together with coalition partners. But combining diverse, multi-national groups can lead to unique challenges in an operational net-centric environment. Component members may differ in basic norms, culture, and beliefs, as well as in areas such as language, doctrine, and policy. Furthermore, differences in procedures can exist – for example, methods of prioritizing and directing resources, and criteria used to measure operational impact and success, may differ between Canadian forces and our allies. In short, there are a significant number of ‘soft ’ issues specific to coalition operations that are expected to negatively impact command and control with respect to time, accuracy, and operational outcome. Moreover, their affect on operational effectiveness will increase when command and control teams are distributed, as in net-centric operations. Implementing appropriate solutions into areas of greatest risk will enhance command and control and reduce the impact of coalition diversity on interoperability. This paper reports on an investigation that identified those areas of greatest concern with respect to the influence of coalition in a Joint Fires Support environment. Based on the findings, recommendations that could ameliorate command and control and improve mission effectiveness are suggested, and future work discussed.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.908 | 0.723 |
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".