Preface: 3rd International Symposium on Frontiers of Economics and Management Science (FEMS 2022)
Bibliographic record
Abstract
It was a great pleasure to welcome you to 3rd International Symposium on Frontiers of Economics and Management Science (FEMS 2022), which had been held successfully during April 29-30, 2022 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 2022 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. This proceeding contains 79 peer-reviewed papers with were selected from more than 120 submissions.
 With our warmest regards,
 Jennifer Bloomer
 Zixin Zhou
 Conference Committees
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".