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Record W4239616338 · doi:10.4018/978-1-61520-889-0

Interprofessional E-Learning and Collaborative Work

2010· book· en· W4239616338 on OpenAlexaff
April Karlinsky

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWork (physics)Collaborative learningPsychologyWork-based learningMedical educationSociologyComputer scienceMathematics educationMedicineEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

FinTech is the term used to refer to financial and technology convergence space solutions. It usually refers to new innovations that conduct or connect with financial services via the internet, smart devices, software applications, or cloud services and encompasses anything from mobile banking to cryptocurrency applications. Despite the advantages of FinTech, cybercriminals seized the opportunity to exploit vulnerabilities in FinTech systems. Phishing attacks, ransomware, and data breaches have become more prevalent, targeting individuals and FinTech institutions. Bahrain, which is not different from the rest of the world, was impacted by such cyber threats. Thus, FinTech companies have had to strengthen their cybersecurity countermeasures and protocols to combat these threats. Existing countermeasures in the literature primarily focus on general cybersecurity practices and frameworks, with limited attention given to the specific needs of the FinTech industry. Hence, there is a notable gap in the literature regarding a focused cybersecurity framework that caters to the unique requirements of FinTech innovations, especially in Bahrain. To bridge this gap, this research addresses the problem by conducting an extensive review of existing cybersecurity challenges, common practices, and cybersecurity standards and through in-depth research interviews with executives, experts, and other FinTech business stakeholders. Leveraging this knowledge, this research proposed an adaptable framework that addresses the risks and vulnerabilities faced by FinTech innovations in Bahrain. Through panel discussions and Delphi sessions, industry experts evaluated the framework’s practical feasibility, ability to address specific risks, and compatibility with the existing FinTech regulatory landscape. The results demonstrate a high acceptance of the developed framework and highlight the framework’s potential to enhance cybersecurity resilience significantly. Moreover, the experts acknowledge the proposed framework as a fundamental baseline in securing the FinTech ecosystem in Bahrain. The importance of this research lies in its potential to enhance the cybersecurity posture of the FinTech industry in Bahrain, mitigating risks and vulnerabilities associated with cyber threats in this vital sector.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.386
Teacher spread0.372 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations16
Published2010
Admission routes1
Has abstractyes

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