Preface: 5th International Symposium on Frontiers of Economics and Management Science (FEMS 2024)
Bibliographic record
Abstract
It was a great pleasure to welcome you to 5th International Symposium on Frontiers of Economics and Management Science (FEMS 2024), which had been held successfully during April 13-14, 2024 in Macao, China. The organizing committees are very grateful indeed to the presence of Prof. W. Wang who from Central China Normal University, and Prof. W. Yue from University of Bedfordshire. We also wish to thank the plenary and invited speakers: Prof. S, Karsten from University of Waterloo, Canada, Prof. G.S. Liu from Hunan University, Prof. S. Zhu from Wuhan University, Prof. P. Wang from Guangzhou College of Commerce. FEMS 2024 had gained respect in the scientific community with consistent hard work of its organizers. The objective of publishing this proceedings is to record, acknowledge, and share the cherished contribution made by the symposium participants. With our warmest regards, Conference Committees
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.158 | 0.104 |
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".