Processual Learning, Environmental Pluralism, and Inherent Challenges of Managing a Socioeconomic Crisis: The Case of the Canadian Mad Cow Crisis
Bibliographic record
Abstract
On May 20, 2003, the report of a single infected cow caused Canada to join the list of countries infected with Bovine Spongiform Encephalopathy (BSE), more commonly known as “mad cow” disease. In this article, by considering the Canadian cattle industry as a political economy, the authors assess factual aspects of the first year of the Canadian BSE crisis from a crisis management perspective. Literature suggests that the processual approach of crisis management can assist marketers in improving their responsiveness to socioeconomic disasters, thereby extending the significance of crisis management theory in marketing. Building a responsive, learning-based approach to crisis management should lead marketers to appreciate the plurality of their environment, the primary source of uncertainty. Building a responsive, learning-based approach to crisis management into the industry will safeguard both the industry and the public against both further socioeconomic crises and further food safety concerns.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".