Proceedings of the 16th European Conference on Knowledge Management
Bibliographic record
Abstract
These proceedings represent the work of researchers presenting at the 16th European Conference on Knowledge Management (ECKM 2015). We are delighted to be hosting ECKM at the University of Udine, Italy on the 3-4 September 2015. The conference will be opened with a keynote from Dr Madelyn Blair from Pelerei Inc., USA on the topic “The Role of KM in Building Resilience”. On the afternoon of the first day Dr Daniela Santarelli, from Lundbeck, Italy will deliver a second keynote speech. The second day will be opened by Dr John Dumay from Macquarie University, Sydney, Australia. ECKM is an established platform for academics concerned with current research and for those from the wider community involved in Knowledge Management to present their findings and ideas to peers from the KM and associated fields. ECKM is also a valuable opportunity for face to face interaction with colleagues from similar areas of interests. The conference has a well-established history of helping attendees advance their understanding of how people, organisations, regions and even countries generate and exploit knowledge to achieve a competitive advantage, and drive their innovations forward. The range of issues and mix of approaches followed will ensure an interesting two days. 260 abstracts were initially received for this conference. However, the academic rigor of ECKM means that, after the double blind peer review process there are 102 academic papers, 15 PhD research papers, 1 Masters research papers and 7 Work in Progress papers published in these Conference Proceedings. These papers reflect the continuing interest and diversity in the field of Knowledge Management, and they represent truly global research from many different countries, including Algeria, Austria, Bosnia and Herzegovina, Brazil, Canada, Chile, Colombia, Cuba, Cyprus, Czech Republic, Estonia, Finland, France, France, Germany, Hungary, India, Indonesia, Iran, Ireland, Italy, Japan, Jordan, Kenya, Lithuania, Mexico, Nigeria, Norway, Pakistan, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, South Africa, Spain, Sri Lanka, Sultanate of Oman, Sweden, Switzerland, Thailand, The Netherlands, UK, United Arab Emirates, USA and Venezuela. We hope that you have an enjoyable conference. Maurizzio Massaro and Andrea Garlatti The University of Udine, Italy September 2015
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.251 | 0.167 |
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".