Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it