Preface: 3rd International Conference on Economic Management and Corporate Governance (EMCG 2023)
Bibliographic record
Abstract
The 2023 3rd International Conference on Economic Management and Corporate Governance (EMCG 2023) was held in Montreal, Canada from October 14-15, 2023. It brings together more than 50 researchers from industry, government, and academia. We invited submissions of papers on all topics related to economic management, business information systems, decision sciences, corporate governance, market structure and pricing. The conference provides opportunities for participants to share ideas, designs, and experiences on the future directions. The conference will feature a high-quality technical & experiential program dealing with a mix of traditional and contemporary hot topics in paper presentations and high-profile keynotes. The conference model was divided into three sessions, including oral presentations, keynote speeches, and Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 10-15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-40 minutes to hold their speeches. Their insightful speeches had triggered heated discussion in the third session of the conference. The EMCG 2023 proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. All the papers have been through rigorous review and process to meet the requirements of international publication standard. We would like to acknowledge all of those who supported EMCG 2023. The help and contribution of each individual and institution was instrumental in the success of the conference. We would like to thank the technical committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. The Organizing Committees of EMCG Montreal, Canada
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".