Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
Bibliographic record
Abstract
For the 6th time, the ACM International Conference Information and Knowledge Management (CIKM) hosts a workshop for PhD students: PIKM 2013: The 6th ACM Workshop for Ph.D. Students in Information and Knowledge Management. The goal of this workshop is two-fold: First, a PhD workshop gives doctoral students an opportunity to present their work in an early stage to a global audience. This allows the students not only to crystallize their ideas into a scientific article, and to practice scientific presentation, but also to receive feedback from reviewers, from fellow students and from the general CIKM audience. Second, we believe that the research community, too, benefits from such a workshop: PhD dissertations are the grassroots of research. They point out new research avenues and indicate current promising topics. They provide fresh viewpoints from the researchers of tomorrow. Also, we hope that the interaction with other researchers at the workshop itself, across all levels of seniority, will help propel science forward. The PIKM workshop covers topics in all core areas of the general CIKM conference: information retrieval (IR), databases (DB), and knowledge management (DB). This diversity of topics got reflected in the submissions we received. The call for papers attracted 13 submissions from all populated continents of the world. Out of these, 6 papers got accepted. The papers cover proposals at various stages of the dissertation, from early outlines of research plans, to in depth investigations of acute questions and mid-term reports of work in progress. The dissertations touch all three main areas of the PIKM, including work on graph clustering, information extraction, and spam detection. This year best submission by Avirup Sil Exploring Re-ranking Approaches for Joint Named-Entity Recognition and Linking receives a special best paper award. As a special highlight, this year's PIKM features a keynote talk by Dr. Pierre Senellart. Dr. Senellart is an associate professor at Telecom ParisTech in Paris. He has published over 30 papers in the area of databases and knowledge management, and will share his advice and experience with the students.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.164 | 0.073 |
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".