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Record W2342206023 · doi:10.1057/rm.2015.12

Navigating the ‘dark waters of globalisation’: Global markets, inequalities and the spatial dynamics of risk

2015· article· en· W2342206023 on OpenAlexfundno aff
Denis Fischbacher‐Smith, Liam Smith

Bibliographic record

VenueRisk Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilMcMaster University
KeywordsGlobalizationTerrorismRisk managementHazardSupply chainBusinessFunction (biology)InequalitySet (abstract data type)Risk analysis (engineering)Industrial organizationEconomicsMarket economyMarketingFinancePolitical scienceComputer science

Abstract

fetched live from OpenAlex

The links between the processes of globalisation and the generation of risk have been brought into sharp focus as a function of a series of accidents and terrorist attacks on Western interests and their supply chains. The increasingly interconnected nature of organisations, the dependence of western economies on a set of global supply chains, the export of hazardous goods and services, and the recruitment of staff from a global recruitment pool, all generate the potential for new forms of hazard and require organisations to reconfigure and extend the capabilities of their control and monitoring systems. Our aim in this article is to review the challenges that globalisation processes can generate for organisational effectiveness and, in particular, on the performance of processes around risk management and the prevention of crises.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.029
Scholarly communication0.0120.016
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.233
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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