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Record W3036916692 · doi:10.5281/zenodo.3894461

Computerization of Banking Operation in Bangladesh

2020· article· en· W3036916692 on OpenAlexaff
Raihan Kibria, Shafkat Reza Chowdhury, Saad Hasan, Mohammad Rashedul Hoque

Bibliographic record

VenueEconstor (Econstor) · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsYork University
Fundersnot available
KeywordsIngenuityContext (archaeology)Work (physics)Scale (ratio)SoftwareEngineering managementEngineeringBusinessComputer scienceRisk analysis (engineering)Economics

Abstract

fetched live from OpenAlex

This paper is about the computerization of the banking operation of two of the largest banks in Bangladesh. The discussion deals with the issues faced in rolling out Core Banking System (CBS) in Sonali Bank and Rupali Bank. The system we studied focuses on centralizing computing operations by consolidating IT infrastructure; this system is meant to replace the existing distributed computing components. In this paper, we will highlight both the technical and human issues we have faced. The paper highlights the context of the work, the scale of the problem, issues and challenges faced during the roll-out. The intricacies of implementing a large-scale system such as CBS are educational, but nevertheless daunting. The findings of the paper articulated that obstacles could be overcome through human ingenuity and discipline. The paper stresses that structural approach of software development is necessary for long-term success of a project and properly trained software professionals is integral to the development of a complex software project.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.219
Teacher spread0.202 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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