Bibliographic record
Abstract
It is our great pleasure to welcome you to the ACM WWW2016 Workshop on Science and Technology for Education (WebED2016), co-located with the 2016 International WWW Conference. This workshop series began as The Workshop on Web-based Education Technologies (WebET) at WWW2014 in Seoul, Korea. However, this year's workshop has expanded its scope to explore the influence the growing field of Science. By doing so it is our goal to bring together educational technologists, researchers, and members of social science communities seeking to investigate the impact of technology on teaching and learning. The mission of the workshop is for attendees to share novel solutions that fulfill the needs of heterogeneous applications and environments and identify new directions for future research and development. It is also our hope that WebED2016 attendees might identify others with similar interests possibly leading to new collaborations and joint efforts.We encourage workshop attendees to attend the keynote speaker presentation, the accepted paper presentations, and the expert panel discussion. Keynote: Web Science, Social Media and Education, Dame Wendy Hall, University of Southampton,Panel: Evaluating Educational Software in the Era, Jutta Treviranus (Ontario College of Art and Design University), Jean-Philippe Bradette (Ellicom), Irwin King (The Chinese University of Hong Kong), Beverly Woolf (University of Massachusetts Amherst), and Irina Muhina (iecarus, moderator)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.757 | 0.627 |
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".