Bibliographic record
Abstract
It is with a great pleasure that we welcome you to the first issue of the Journal of Global Business Insights (JGBI). JGBI, originally established in 2015, was known as International Interdisciplinary Business-Economics Advancement (IIBA) Journal from 2015-2018. This journal was born with several things in mind. In today’s world, globalization is becoming the norm, thanks to the technology. For this reason, we wanted to create a journal that has a global focus. The journal welcomed distinguished, global editorial board. We would like to thank each of them for their dedication for a new journal. We also wanted to create a true open-access journal without any commercial interests. JGBI is created, managed and maintained by true volunteers. It is with a great pride to tell that sending papers, publishing papers and accessing them are 100% free. JGBI aims to provide an intellectual platform and ideas for international scholars, by promoting interdisciplinary studies related to business and social science. With this goal in mind, we welcome you to the Volume 3 – Issue 2, the first issue under the JGBI. Our first issue present five great articles. The first paper by Zhang Ying and Cainan Zhang presents case study of comprehensive benefit evaluation and management of forest ecosystem services in Zhalantun city of Inner Mongolia, China. Second article by Marica Mazurek discusses smart management systems (Triple Helix model) in Waterloo, Canada. On another dimension, third article by Rupam Konar and Kashif Hussain investigates the expenditure and experience of international conference delegates visiting Malaysia. Fourth article by Laiba Ali, Wong F. Yee, Ng S. Imm, and Muhammad S. Akhtar discusses the price fairness, guest emotions, satisfaction, and behavioral intentions in peer to peer accommodation sector. Finally, Tingting Zhang presents employee wellness innovations in hospitality workplaces: learning from high-tech corporations. We welcome your comments and suggestions. In the future issues, we would like to integrate new sections into JGBI such as industry insights, case studies, book reviews, and opinion papers. We would like to thank Association of North America Higher Education International (ANAHEI) and University of South Florida, Scholar Commons for publishing JGBI and making it accessible to the world population. We believe that with your support, JGBI will be indexed in major indexes in a short time. We also would like to invite you submit an article to JGBI. In addition, we welcome your proposals for a special issue.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.131 | 0.114 |
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".