Bibliographic record
Abstract
Advancements in technology are regularly identified, assessed, and classed into emerging and/or potentially disruptive technologies, according to their ability to cause disruptions to defence systems, and in defence. Perhaps this is because defence capabilities centre on grand technology systems deployed at the level of nations. Hypersonic missiles are one example. The testing of a new hypersonic missile or a research program on types of hypersonic drones immediately sparks questions like: which other nations have such capability? or what types of technologies can be used to detect or counter these? In contrast, the ability to identify weak, faint factors that add up and lead to conflict are not brought together in a systematic manner. Nor is it common for there to be a cross-talk between a combination of methods used within military science and technology organizations over in to social sciences related to intelligence and/or conflict. This is a preventable strategic foresight issue relevant for enhancing, planning for, and investing in the security space. This paper describes the MAD (Methodology for Assessing Disruptions) tool, which is adaptable beyond the defence domain. MAD is a scenario-based two-part table-top exercise conducted to identify weak signals that have the potential to cause disruptions, which by consequence may coalesce into challenges for security. Exercising such methods is essential for security professionals to prepare and plan for future conflicts instead of constantly reacting to immediate acute problems.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.022 |
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".