Preface: 5th International Conference on Management Science and Innovative Education (MSIE 2023)
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.233 | 0.154 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".