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Record W4318459875 · doi:10.54097/hbem.v3i.3054

Preface: 4th International Conference on Management Science and Innovative Education (MSIE 2022)

2023· article· en· W4318459875 on OpenAlexaboutno aff
Changcheng Zhou, Alberta Obadiah

Bibliographic record

VenueHighlights in Business Economics and Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)ChinaPolitical sciencePublic relationsProcess (computing)Engineering ethicsLibrary scienceSociologyMedical educationEngineeringComputer scienceMedicineLaw

Abstract

fetched live from OpenAlex

The 4th International Conference on Management Science and Innovative Education (MSIE 2022) was held from December 30-31, 2022 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 2022 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 2022 has received 179 manuscripts. By submitting a paper to MSIE 2022, 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 2016 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 5th 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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

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

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 head0.228
Teacher spread0.212 · 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 designTheoretical or conceptual
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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