Bibliographic record
Abstract
It is our great pleasure to welcome you to BigScholar 2016, The Third WWW Workshop on Big Scholarly Data: Towards the Web of Scholars. The workshop is held in Montreal, Canada, April 2016, as part of the 25th International World Wide Web Conference (WWW 2016).The BigScholar workshop aims at bringing together researchers and practitioners working on Big Scholarly Data to discuss what are emerging research issues and how to explore the Web of Scholars. Several core challenges, such as the tools and methods for analyzing and mining scholarly data will be the main center of discussions at the workshop. The goal is to contribute to the birth of a community having a shared interest around the Web of Scholars and exploring it using data mining, recommender systems, social network analysis and other appropriate technologies.In response to the call-for-papers, this third edition of the workshop received 22 submissions from Asia, Europe, South America, Canada, and the United States of America. Each paper was reviewed by at least two members of the program committee. As a result of the rigorous review process, 12 high-quality papers were accepted for presentation at the workshop and inclusion in the proceedings. In addition to paper presentations, the workshop also features two Invited Keynote Speeches delivered by Prof. C. Lee Giles from Pennsylvania State University and Prof. Jie Tang from Tsinghua University, respectively.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.683 | 0.549 |
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".