Proceedings of the CAiSE*03 10th Doctoral Consortium on Advanced Information Systems Engineering
Bibliographic record
Abstract
The papers published in these proceedings were presented at the 10th Doctoral Consortium of the Conference on Advanced Information Systems Engineering, taking place in Velden, Klagenfurt, Austria on June 16-17, 2003. Starting in 1994, the Doctoral Consortium has been held annually during the CAiSE conference, in Utrecht (Netherlands, 1994), Jyvaskyla (Finland, 1995), Heraklion (Greece, 1996), Barcelona (Spain, 1997), Pisa (Italy, 1998), Heidelberg (Germany, 1999), Stockholm (Sweden, 2000), Interlaken (Switzerland, 2001), and Toronto (Canada, 2002). The Doctoral Consortia on Advanced Information Systems Engineering are intended to bring PhD students together within the information systems engineering field, and give them an opportunity to present and discuss their research in a constructive and international atmosphere. They are accompanied by prominent professors in the information systems engineering field that provide feedback on the research work presented by the PhD students. Submissions to the Doctoral Consortia are extended abstracts of ongoing PhD research. For the 10th Doctoral Consortium of CAiSE*03, 10 submissions were accepted and presented at the workshop. The papers demonstrate a variety of research topics and approaches, covering research in: business process modelling and integration, events in active systems, semantic interoperability, Information and Communication Technologies (ICT), mobile agents, e-commerce consumers, data management, data quality, and knowledge-based methods for Asbru protocols. During the workshop, each paper was introduced by a 20 minute presentation followed by a 25 minute discussion of the topic, research approach and research limitations. Furthermore, during the consortium, the professors gave talks on general questions related to PhD research, and participated in a panel debate on what constitutes a good PhD thesis. Our special thanks go to the accompanying professors at the consortium: Jeffrey Parsons (Faculty of Business Administration, Memorial University of Newfoundland, Canada), Richard Welke (J. Mack Robinson College of Business, Georgia State University, USA), Sudha Ram (College of Business and Public Administration, University of Arizona, USA), and Hans Oppelland (Faculty of Economics, Erasmus University of Rotterdam, Netherlands). The Doctoral Consortium would not have been possible without their valuable contributions. We would also like to thank the participants of the 2002 Doctoral Consortium. They have been involved in the reviewing process, several of them for the first time. Furthermore, we thank all the participants for their great interest and effort displayed both in the preparation and presentation of their work, as well as in the discussion of the contributions of others. Finally, we sincerely thank the organising committee and local organisers of the CAiSE*03 conference, for their support in preparing the Doctoral Consortium. Velden, June 2003 Joerg Evermann, Eva Soderstrom, Julia Kotlarsky.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.132 | 0.043 |
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".