Protecting civilians in a data-driven and digitalized battlespace: toward a baseline humanitarian technology infrastructure
Bibliographic record
Abstract
This article examines the realities of modern day warfare, including a rising trend in hybrid threats and irregular warfare which employ emerging technologies supported by digital and data-driven processes. The way in which these technologies become applied generates a widened battlefield and leads to a greater number of civilians being caught up in conflict. Humanitarian groups mandated to protect civilians have adapted their approaches to the use of new emerging technologies. However, the lack of international consensus on the use of data, the public and private nature of the actors involved in conflict, the transnational aspects of the widened battlefield, and the heightened security risks in the conflict space pose enormous challenges for the protection of civilians agenda. Based on the dual-usage aspect of emerging technologies, the challenges associated with regulation and the need for those affected by conflict to demonstrate resilience toward, and knowledge of, digital media literacy, this paper proposes the development of guidance for a "minimum basic technology infrastructure" which is supported by technology, regulation, and public awareness and education
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".