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Record W4408316049 · doi:10.71427/icaeed2025/31

Journey of Bangladeshi Engineering Education to Become a Full Signatory to the Washington Accord in 2024

2024· article· en· W4408316049 on OpenAlexaboutno aff
A H M Kamruzzaman, Ataur Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Economic Relations
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The accreditation process of engineering degree programs, governed by the Washington Accord, aims to benchmark engineering education across different countries against a set of criteria. This paper presents the journey of Bangladesh to become a full signatory to the Washington Accord with the anonymous support from twenty-three signatory countries of the International Engineering Alliance (IEA) on the 12 June 2024. The Board of Accreditation for Engineering and Technical Education (BAETE), established in 2003 as an independent body of the Institution of Engineers Bangladesh (IEB), is responsible to accredit Bachelor of Engineering degrees offered by Bangladeshi universities and colleges. As Bangladeshi engineering degrees were not accredited before, the engineers were facing multiple challenges to work in the developed countries like the USA, UK, Australia and Canada. These include higher degree research, dignified jobs, bilateral research, foreign investment in Bangladesh, immigration and others. The Australian chapter of IEB played a significant role in achieving this recognition as presented in this paper. It is expected that this recent recognition of Bangladeshi engineering programs will enhance the collaboration of engineering disciplines of both Australia and Bangladeshi universities. The outcome-based engineering educations in Bangladesh as recognized by the Washington Accord will assist Bangladeshi engineers to meet the growing challenges of sustainable development in Bangladesh and other full signatory member countries of Washington Accord.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.303
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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