Governance Vacuum Correction Standard (GTS v5.1): A Non-Binding Framework for Institutional Latency Reduction in High-Risk Environments
Bibliographic record
Abstract
TSUMS 4.0V (BOI Edition) introduces a non-interventionist governance interface designed for high-risk mountain and disaster environments. This document defines a replayable governance structure that enables institutions to verify whether decisions were made under pre-committed parameters at the time of execution. The framework does not prescribe decisions, assign liability, or override sovereign authority. It provides a deterministic and auditable structure for: • Risk-state traceability • Institutional response latency visibility • Decision replay verification • Accountability mapping under pre-locked governance parameters TSUMS 4.0V operates as a policy-neutral interface layer. Normative judgment and legal authority remain sovereign. This BOI (Boundary of Intervention) edition clarifies that TSUMS functions strictly as a transparency and replay standard, not as a regulatory mechanism. The objective is to reduce post-incident ambiguity in high-risk environments by preserving governance memory through deterministic structural replay. Adoption is voluntary.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".