<em>Editor's Introduction</em> The Cost of Fear
Bibliographic record
Abstract
No human fatalities occurred when US Airways Flight 1549 crash-landed in the Hudson River after colliding with a flock of Canada geese on January 15, 2009(Caudell 2009).More broadly speaking, I am not sure the announcement of no fatalities was accurate, however.Admittedly, none of the plane's passengers and crew members was killed in the incident, but the collision of the aircraft with birds will reinforce a fear-of-flying that grips millions of people worldwide.As a result of this widely-publicized crash, many people will now drive to their destinations, rather than fly.Statistically, driving places people in greater peril than does flying.If one of the people who decided to drive rather than fly is killed in a car accident, it could be argued that US Airways Flight 1549 has suffered its first fatality, because someone indirectly has lost his or her life as a result of fear created by that airplane crash.This scenario suggests one of the more serious, but unreported, costs of human-wildlife conflicts: fear of wildlife.Wildlife phobias are common and serious.They include fears about being attacked by a predator, bitten by a snake or rabid animal, or killed in airplane crash as a result of a bird strike.Victims of wildlife phobias suffer a diminished enjoyment of life.Economists use the term lost-opportunity cost when they refer to the costs of forgoing opportunities with a resulting diminishment in life's joys.Lost-opportunity costs caused by wildlife also include economic losses suffered when someone is unable to take advantage of an opportunity because of a problem with wildlife.For instance, a farmer may not be able to use a pasture for grazing because he is afraid that coyotes will kill any livestock placed in the pasture.Unfortunately, lost-opportunity costs are rarely considered or quantified when documenting the cost of human-wildlife conflicts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".