Preface: 4th International Conference on Global Business and Management Science (GBMS 2024)
Bibliographic record
Abstract
The 2024 4th International Conference on Global Business and Management Science (GBMS 2024) was held during July 13-14, 2024, in Vancouver, Canada. It is one of the conferences for presenting novel and fundamental advances in the fields of global business, economic systems, corporate governance, financial economics, management science, marketing research, and human resource. GBMS 2024 provides an excellent international platform for the academicians, researchers, and industrial experts from around the world to share their research findings with the global experts. The event is also an opportunity for PhD students in this area to moot their dissertation works to a global audience. The key intention of this seminar is to provide opportunity for the global participants to share their ideas and experience in person with their peers expected to join from different parts of the world. In addition, this gathering will help the delegates to establish research and business relations and linkage for future collaborations in their career path. We hope that the outcome of this conference will lead to significant contributions towards creation of new knowledge. The idea of the GBMS 2024 is for the scientists, scholars, engineers and students from the universities all around the world and the industry to present ongoing research activities, and hence to foster research relations between the universities and the industry. There is a real opportunity among a wide range of scientists, teachers, industry representatives, and students in various fields related, to exchange ideas, share knowledge and establish close cooperation. Regards, The Organizing Committees of GBMS Vancouver, 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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.212 | 0.148 |
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".