Bibliographic record
Abstract
It is with great pleasure, and on behalf of the organizing committee, we would like to welcome you to the 8th International Workshop on Web Intelligence & Communities (WI&C 2016) taking place on April 11th in Montreal, Canada and collocated with the WWW 2016 conference.This workshop, the eighth in a series of workshops, is intended to stimulate discussions on the forefront of research concerned with web intelligence applied to collaborative networks. Web Intelligence consists of a multidisciplinary area dealing with exploiting data and services over the Web, to create new data and services using both Information and Communication Technologies (ICT) and Artificial Intelligence (AI) techniques. Communities appear as a first-class object in the areas of web intelligence and agent technologies, as well as a crucial crossroads of several sub-domains (i.e. user modelling, protocols, data management, data mining, content modelling, etc.). These sub-domains impact the nature of the communities and the applications which are related to them. These applications are numerous, and the success of well-known Social Network Sites for entertainment should not be allowed to over-shadow the other application domains, for instance in education, health, design, knowledge management, and so forth.The workshop will provide presentation and discussion opportunities for researchers working on web intelligence applied to collaborative networks, such as virtual communities. The possibilities and consequences of the web usage for collaborative networks are tremendous and new tools are required to satisfy users and service providers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.737 | 0.682 |
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".