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Record W1986651711 · doi:10.1108/00251740410542357

The new normal: lessons learned from SARS for corporations operating in emerging markets

2004· article· en· W1986651711 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.

Bibliographic record

VenueManagement Decision · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsWarrantGuard (computer science)Emerging marketsPreparednessBusinessCrisis managementPandemicGlobalizationOutbreakCoronavirus disease 2019 (COVID-19)Inclusion (mineral)Development economicsEconomic growthEconomicsMarket economyInfectious disease (medical specialty)FinanceManagementDisease

Abstract

fetched live from OpenAlex

The modern industrialized world was completely caught off guard by the recent SARS outbreak. Fortunately, for most organizations, the impact has been short lived, but management has been provided with a reminder of the impact of the external environment in a world of ever increasing globalization. As seen with the SARS outbreak, a lack of preparedness can have devastating effects on business and warrant inclusion in a business definition of a crisis. This paper uses the recent SARS epidemic as a background to highlight the importance of crisis planning, particularly in emerging economies, and suggests how organizations can address these 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.054
GPT teacher head0.362
Teacher spread0.308 · 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