Governance, Innovation, and Information and Communications Technology for Civil-Military Interactions
Bibliographic record
Abstract
<p class="p1">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.
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 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 | 0.002 | 0.001 |
| 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".