Bibliographic record
Abstract
This work introduces the concept of self-organizing security (SOS) network as a resilient architectural foundation on which the operational mechanism for deploying dynamic, short living emergency response organizations capable to react quickly to emerging crisis situations can be evolved. A simulation testbed for SOS networks is presented that balances micromanagement of subordinates with the excessive independence of commanders based on a trusted overall operational picture shared via a joint communications backbone. Built on the foundation of the recently introduced emergent engineering paradigm, the SOS testbed delivers a picture of the dynamics of emerging trends that enable decision makers to anticipate the evolution of emerging crises and evaluate the effectiveness of different inter-agency configurations coming together in addressing it. Hints towards a dasiachange of culturepsila shifting first responders operations from the traditional hierarchical towards a dasiapower to the edgepsila heterarchy point to policy changes that allow emerging leaders to take action in the dasiachaos of crisispsila. The strategies proposed increase the responsiveness and effectiveness of first responder meta-organizations thus reducing the vulnerabilities to asymmetric threats to increase the safety quotient and by this the social resilience in today's convoluted world dynamics.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".