Bibliographic record
Abstract
Welcome to the proceedings for the Seventeenth International Conference on Management Science and Engineering Management (ICMSEM 2023), which was held from the 3rd to the 4th of August 2023 at the University of Cape Town, Cape Town, South Africa.Organized by the International Society of Management Science and Engineering Management (ISMSEM), this annual Conference provides a platform for academics, researchers, and practitioners from Management Science (MS), Engineering Management (EM), and related fields to gain advanced knowledge about MSEM research and technologies and gives participants the chance to work together and share the newest MSEM developments.Because new findings are emerging every year, participants have the opportunity to explore advanced methodologies and technologies that can address new global challenges.Every year, an increasing number of prestigious experts and scientists from all over the world attend the Conference to share their innovative work.Therefore, we hope that everyone can gain something by freely communicating with peers and colleagues during the conference.The ICMSEM, which has been held sixteen times since 2007 in locations across Asia, Europe, the Americas, and Oceania has significantly influenced Management Science and Engineering Management research.In the past sixteen years, the ICMSEM has been successfully held in Chengdu, Chongqing, Bangkok, Chungli, Macau, Islamabad, Philadelphia, Lisbon, Karlsruhe, Baku, Kanazawa, Melbourne, Ontario, Chisinau, Toledo, and Ankara.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.479 | 0.307 |
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".