Preface for the IEEE Conference Proceedings of the 9th International Conference on Information Management at the University of Oxford, England, UK
Bibliographic record
Abstract
It is my great pleasure to introduce the Proceedings of the 9th International Conference on Information Management (ICIM 2023). The event was held at Worcester College, the University of Oxford, England, the United Kingdom, from the 17th to the 19th of March, 2023.\n\nOne of the meaningful and valuable dimensions of this conference is the way it brings together researchers, scientists, academics, and practitioners in the fields from different countries, and enables discussions and debate of relevant issues, challenges, opportunities, and research findings. The primary focus of ICIM 2023 is to provide an excellent platform for the participants to share and exchange brilliant ideas and excellent outcomes of original research, and to build international links. We deliver our promise on helping create a bright picture and charming landscape for the areas of information management and systems.\n\n54 submissions were accepted as full papers for publication and presentation in ICIM 2023, with authors from the United Kingdom, China, USA, Switzerland, Saudi Arabia, Japan, India, Canada, Malaysia, Germany, Croatia, Thailand, Qatar, Kuwait, and other countries. These papers provide good examples of current research on relevant topics, covering system models and data, data analysis and engineering, intelligent information systems, multi-agent-based systems, e-commerce and economic management, education technology, etc. \nI would like to thank the authors for having attended the conference and having brought their expertise and research findings to this event. Their vision, contributions and participation will help us build and pave the way into the future of information management, systems, applied artificial intelligence, the Metaverse and relevant areas. They are the greatest assets and players today and tomorrow. I believe that the authors and delegates enjoyed their stay at Oxford, England, and have found ICIM 2023 interesting, exciting and inspiring.\n\nWe would like to express and record our gratitude and appreciation to the reviewers who helped us maintain the high quality of manuscripts included in the Proceedings published by IEEE. We also express our sincere thanks to the members of the conference committees and organising team for their hard work. \n\nWith very best wishes and kindest regards\n\nShuliang Li\nConference Chair of ICIM 2023 at Oxford Worcester College, University of Oxford, UK\nFellow (life member) of the British Computer Society\nThe University of Westminster, UK & Southwest Jiaotong University, China\n\n
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.479 | 0.275 |
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".