Call for Papers & Editorial Board Members for Global Empirical Marketing Studies (GEMS)
Bibliographic record
Abstract
This is a poster with the following content. It is useful for content analysis research, art design analysis, machine learning, communication analysis, advertisement research, and so on. CALL FOR PAPERS & EDITORIAL BOARD MEMBERS Aims and Scope of the Journal Global Empirical Marketing Studies (GEMS) seeks to publish high quality, peer-reviewed articles in consumer behavior, marketing, international business, and hospitality: •Empirical research papers •Theoretical/conceptual papers •Case studies •Literature reviews •Perspective/opinion articles Why Publish Papers in GEMS? 1. Articles published in GEMS can be seen, mentioned, and cited by a wide audience: •Free to read and download all articles. •Permanently archived at Zenodo with valid DOI. •Indexed by Zenodo, OpenAIRE, and Google Scholar. •Actively promoted on major research and social media platforms, such as LinkedIn, X, ResearchGate.net, and Academia.edu. 2. No fees for manuscripts submitted by June 30, 2025. 3. Fast, helpful, and reasonable review process. 4. Truly credible advisory and editorial boards. 5. Simple submission method—just e-mail the manuscripts prepared in any style. Joining GEMS Editorial Board Scholars with PhDs in relevant fields are invited to join our editorial board. Please contact the Chief Editor today! GEMS Chief Editor Associate Professor Dr. Chanthika Pornpitakpan PhD, University of British Columbia, Canada Top 2% scientists of the world since 2019 GEMS2025@yahoo.com https://sites.google.com/view/gems-team https://gems.wuaze.com/index.php
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 | 0.031 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.525 | 0.511 |
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".