Holy mad cow! Facts or (mis)perceptions: A clinical study
Bibliographic record
Abstract
Abstract The May 20, 2003, announcement confirming diagnosis in a Canadian cow of mad cow disease caused price disturbances in livestock, grain, and stock markets. Price and time data are used to provide a clinical study on the timing, persistency, and rationality of those disturbances in different U.S. markets, showing the three types of uncertainty that C. Avery and P. Zemsky (1998) use to identify herd behavior and the resulting mispricing. Markets react at different times, showing an informational cascading pattern. Misperceptions cause futures contract and stock reactions that are unsupported by the facts. Livestock and grain futures markets reactions suggest that people would replace beef with pork. Biogenetic stocks show price disturbances for companies with no relation to screening or treatment for mad cow disease. The market reactions to the December 23, 2003, announcement of the first incidence of mad cow disease in the United States are examined to see whether the markets have learned from the May event. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:315–341, 2006
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".