Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Bibliographic record
Abstract
For the 4th time, the International Conference on Information and Knowledge Management (ACM CIKM) hosts a workshop for Ph.D. students: PIKM 2011. The goal of this workshop is two-fold: First, a Ph.D. 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: Ph.D. theses 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. Last, 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 (KM). This includes subjects as diverse as resource monitoring, semantic search, pattern recognition, data mining, and data warehousing. This diversity of topics was reflected in the submissions we received. The call for papers attracted 18 submissions from nearly all continents of the world. Out of these, 9 papers were accepted as full papers. In addition, 4 papers were accepted as poster papers. The papers cover proposals at various stages of the dissertation, from early outline of research plans, to in-depth investigations of acute questions and mid-term reports of work in progress. The dissertations touch all main areas of the PIKM including, for example, work on user interaction and ranking, as well as research on workflow management. Similar to past PIKM workshops, the best submission will receive a best paper award. This year's award will go to Minsuk Kahng, Sangkeun Lee and Sang-Goo Lee for their paper Ranking Objects by Following Paths in Entity-Relationship Graphs. As a special highlight, this year's PIKM features a keynote talk by Prof. Dr. Felix Naumann from the Hasso-Plattner-Institute, Potsdam, Germany. Prof. Naumann will talk about the challenges of Extreme Web Data Integration -- a task that becomes ever more challenging with the relentless growth of the Web.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.256 | 0.149 |
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".