Bibliographic record
Abstract
It is our great pleasure to welcome you to the PhD Symposium that is held in conjunction with the 25th International World Wide Web Conference, April 11 -- April 15, 2016, Montreal, Canada. The PhD Symposium of WWW2016 provides an excellent opportunity for PhD students at different stages in their research to present their ideas, and receive feedback on their work by experienced researchers and other PhD students working in research areas related to the World Wide Web.The call for papers attracted 16 submissions from Brazil, Canada, China, France, Germany, Greece, India, Ireland, United Kingdom, and the United States. The program committee reviewed and accepted 7 papers that cover a variety of topics including search and recommendation, web mining, social networks and graph analysis, crowdsourcing analysis, semantics and big data, among others. We hope that the program will serve as a valuable reference for researchers and developers in the field of World Wide Web.Putting together the WWW2016 PhD Symposium was a team effort. We first thank the authors for their contributions to the program. We must also thank the program committee members for their invaluable efforts in reviewing papers and providing constructive feedback to authors. We are also grateful to the General Chairs, James Hendler and Roger Nkambou, the Local Organization Committee Members and ACM SIGs for their guidance, support and great help in the preparation and organization of this program.We hope that you will find this program interesting and thought-provoking and that the PhD Symposium will continue its excellence and serve as an important forum for PhD candidates around the world to share their original research results in the field of World Wide Web.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.577 | 0.459 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".