Governance, Innovation, and Information and Communications Technology for Civil-Military Interactions
Bibliographic record
Abstract
Civilian and military participants in relief and stability operations rely upon Information and Communications Technology (ICT) to collect, analyze, store, display, and share information that is critical for these civil-military interactions. This article investigates ICT innovation in these operations over time. As researchers in the sociology of technology school might predict, ICT innovation for relief and stability operations emerges in a distributed fashion, within clusters of specialty expertise that migrate across interconnected technology systems and across humanitarian and military activities. Major events such as natural disasters have punctuated the development of ICT for civil-military interactions, often driving community learning and coherence. Among the many stakeholders in the United States, the federal government in particular has played an important role in shaping the ICT ecosystem through policies and engagements. Government policies and changes in the field of action in the 1990s created imperatives for the US military in particular to collaborate with civilian agencies on ICT innovation. Civil-military information sharing gaps persist today due, in part, to institutional factors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".