Operationalising Human Security in the Contemporary Operating Environment
Bibliographic record
Abstract
Drawing upon primary research funded by the UK Defence and Security Accelerator (DASA), this article is about using data analytics and artificial intelligence (AI) for operationalising human security in the contemporary operating environment. The idea of human security has gained much traction in the international community since its introduction in a 1994 United Nations Development Programme (UNDP) report and has more recently become a military concern. Yet, the core tenets of this idea remain contested, and the military role in support of human security remains an open question. Nonetheless, the concurrent increase in Open Data and AI does give rise to new opportunities to understand the various human security concerns. In response, DASA funded Projects SOLEBAY and HAMOC to research these concerns and the possibilities of data analytics for human security. Drawing on the research findings, we propose the idea of Population Intelligence (POPINT) as a new intelligence discipline to operationalise human security.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".