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Record W4390235061 · doi:10.62051/jt2j1t54

Preface: 5th International Conference on Management Science and Innovative Education (MSIE 2023)

2023· article· en· W4390235061 on OpenAlexaboutno aff
Changcheng Zhou, Alberta Obadiah

Bibliographic record

VenueTransactions on Economics Business and Management Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Political scienceChinaPublic relationsEngineering ethicsProcess (computing)Library scienceMedical educationEngineeringMedicineComputer scienceLaw

Abstract

fetched live from OpenAlex

The 5th International Conference on Management Science and Innovative Education (MSIE 2023) was held from November 25-26, 2023 in Singapore. This conference was sponsored by Research Center of Engineering and Science, and was attended by academicians, researchers, entrepreneurs, government agencies and policy-makers, postgraduates from China and all over the world.
 The aim of MSIE 2023 is to provide a platform for educators, scholars, managers and graduate students from different cultural backgrounds to present and discuss research, developments and innovations in the fields of Social Science. It provides opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration.
 MSIE 2023 has received 87 manuscripts. By submitting a paper to MSIE 2023, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the Conference Committee, who will give detailed comments and-if the submission gets accepted-the authors submit a revised version that takes into account this feedback. All papers are reviewed using a double-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. The Committees of MSIE invest great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit.
 Hopefully, all participants and other interested readers benefit scientifically from the proceedings and also find it stimulating in the process. We look forward to the exciting moments of the 6th International Conference on Management Science and Innovative Education next year.
 Changcheng Zhou, Alberta Obadiah
 Conference Organizing Committee

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.068
GPT teacher head0.327
Teacher spread0.259 · 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 designOther design
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
Published2023
Admission routes1
Has abstractyes

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