Human–Machine Social Systems: Test and Validation via Military Use Cases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global commercial leaders (e.g. Google, Amazon, and Toyota) and governments around the world are heavily investing in intelligent, bidirectional interactions between humans and technologies that involve complex social interactions. The military sector, in particular, is investing in modernization strategies that target artificial intelligence/machine learning (AI/ML) techniques that lend themselves to human–machine teaming in order to prepare for a future of multidomain operations. As with the pioneering empirical approach to human assessment and selection by global military leaders post–World War I [16], the immediacy, complexities, size, diversity, and resource capability of military use cases can generate the foundational underpinnings for shared problems, such as human-machine systems (HMS). When executed with strategic partners (e.g. commercial sector, partner nations), these underpinnings can be extrapolated and validated in multiple application domains. This chapter outlines key social cognitive complexities best examined in situ with real users, and highlights collaboration opportunities with the U.S. military (e.g. Army Project Convergence) as one potential path for in situ test and validation.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it