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Record W2261127938 · doi:10.1177/1086026616629794

How Firm Responses to Natural Disasters Strengthen Community Resilience

2016· article· en· W2261127938 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

VenueOrganization & Environment · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNatural disasterCommunity resilienceResilience (materials science)TypologyStakeholderPublic relationsBusinessStakeholder theoryEmergency managementGovernment (linguistics)SociologyEconomicsPolitical scienceEconomic growthEngineering

Abstract

fetched live from OpenAlex

Natural disasters challenge a community’s resilience. Prior community resilience research has focused on the responses of public entities, such as emergency services and government agencies. However, for-profit firms are also engaged in responding to natural disasters. This article explores two aspects of how firms participate in building community resilience to natural disasters: First, the article synthesizes research on business continuity management, corporate philanthropy, and emerging evidence that firms engage in the business of disaster response into a coherent typology of for-profit firm responses to natural disasters. Second, the article draws on stakeholder theory to distinguish between firms adopting firm-centric postures (focused inwardly on firm outcomes) versus firms adopting community-centric postures (focused outwardly on stakeholders), with respect to responding to natural disasters. We theorize relationships between firm- versus community-centric postures and different community resilience outcomes. The article concludes by discussing contributions to stakeholder theory and outlines future research directions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.237
Teacher spread0.223 · 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