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Record W1987661036 · doi:10.1177/0276146707300066

Processual Learning, Environmental Pluralism, and Inherent Challenges of Managing a Socioeconomic Crisis: The Case of the Canadian Mad Cow Crisis

2007· article· en· W1987661036 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Macromarketing · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsYork UniversityUniversity of Regina
Fundersnot available
KeywordsCrisis managementSocioeconomic statusBovine spongiform encephalopathyPluralism (philosophy)Risk managementPoliticsBusinessPublic relationsPolitical scienceEnvironmental healthLawDiseaseMedicinePopulation

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it