Bibliographic record
Abstract
This paper examines 10 sources of uncertainty in any risk-management system and illustrates them in security measures against terrorism. First, any risk assessment is an uncertain knowledge claim about contingent future events that cannot be fully known. Second, only some risks can be selected for attention, and those left unattended are sources of uncertainty. Third, specific decisions in risk management bear the uncertainty of false positives and false negatives. Fourth, risk-management technologies manufacture new uncertainties, some of which pose risks greater than those they were designed to control. Fifth, risk is reactive: as people act on knowledge of risk, they simultaneously change the risk environment and create new uncertainties. Sixth, the complexity of risk-management systems can result in multiple and unexpected failures occurring simultaneously; such "normal accidents" are a source of uncertainty beyond any direct human capacity for control. Seventh, catastrophic failures result in the urge to risk manage everything: intensified surveillance, audit, and regulation increase system complexity and yield more uncertainty. Eighth, risk managers facing an increasingly litigious environment for failures become defensive, focusing more on operational risks that might affect the reputation of their organization than on the real risks they are supposed to manage. Ninth, excessive precaution escalates uncertainty and breeds fear, leading to risk-management measures that are at best misplaced and at worst incubate new risks with catastrophic potential. Tenth, risk-management systems can restrict freedom, invade privacy, discriminate, and exclude populations. Such self-defeating costs and the uncertainties they entail can be minimized only by infusing risk-management systems with value questions about human rights, well-being, prosperity, and solidarity.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".