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Record W4386753465 · doi:10.24191/apmaj.v18i2-11

The Social Construction of Risk: Evidence from UK Banks

2023· article· en· W4386753465 on OpenAlexaff
Dominic Roberts, Ekililu Salifu

Bibliographic record

VenueAsia-Pacific Management Accounting Journal · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsMacEwan University
Fundersnot available
KeywordsRisk perceptionRisk managementSocial riskPerceptionConstruct (python library)Social constructionismContext (archaeology)Financial risk managementBusinessEmpirical researchOperational riskEmpirical evidenceIT risk managementIT riskEnterprise risk managementPublic relationsMarketingActuarial scienceSociologyPsychologyFinancePolitical scienceComputer scienceSocial scienceEpistemology

Abstract

fetched live from OpenAlex

This research presents empirical evidence of the perceptions of risk in UK operating banks and how these perceptions influence risk processes at these institutions. We used social constructivism to understand the views of UK managers in the banking industry. The study found that there was a divide in risk perception among risk managers in UK operating banks. Such a divide is crucial in explaining the differences in risk approach and risk processes in the banking industry. The discussion presented is based on the results of 25 semi-structured interviews. Two distinct characterizations of risk emerged from the data. One perceived risk as a calculable, measurable construct that can be managed, controlled and verified. The other conceived risk as a mixture of mathematical numerics and social ideals that engages an understanding of and appreciation for the concept. Each viewpoint represents an opportunity to fathom risk in its own context, contributing to the critical debate on risk management. The extent to which social factors influence risk decisions varied among banking institutions. Keywords: risk, risk management, social construction, banks

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.242
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 teacher head, not a consensus.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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