Information use and early warning effectiveness: Perspectives and prospects
Bibliographic record
Abstract
Abstract This introductory article explores how the use of information affects the effectiveness of early warning systems. By effectiveness, we refer to the capacity of the system to detect and decide on the existence of a threat. There are two aspects to effectiveness: (a) being able to see the evidence that is indicative of a threat and (b) making the decision, based on the weight of the evidence, to warn that the threat exists. In early warning, information use is encumbered by cues that are fallible and equivocal. Cues that are true indicators of a threat are obscured in a cloud of events generated by chance. Moreover, policy makers face the difficult decision of whether to issue a warning based on the information received. Because the information is rarely complete or conclusive, such decisions have to consider the consequences of failing to warn or giving a false warning. We draw on sociocognitive theories of perception and judgment to analyze these two aspects of early warning:detection accuracy(How well does perception correspond to reality?) anddecision sensitivity(How much evidence is needed to activate warning?) Using cognitive continuum theory, we examine how detection accuracy depends on the fit between theinformation needs profileof the threat and theinformation use environmentof the warning system. Applying signal detection theory, we investigate how decision sensitivity depends on the assessment and balancing of therisks of misses and false alarmsinherent in all early warning decision making.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".