Bibliographic record
Abstract
The 2nd International Conference on Social Computing and Its Applications (SCA2012) was held in Xiangtan, China, November 1-3, 2012. SCA (Social Computing and its Applications) is created to provide a prime international forum for researchers, industry practitioners and environment experts to exchange the latest fundamental advances in the state of the art and practice of social computing and broadly related areas. SCA2012 consists of the main conference and three workshops: the 2012 International Workshop on Social Network Analysis and Information Diffusion Modelling (SNAIDM2012), the 2012 International Workshop on Web Wisdom (WW2012), and the 2012 International Workshop on Social Network Service on Databases (SNSDB2012). We greatly thank the Workshop Chairs for their valuable time and effort in organizing the workshops. SCA2012 is held jointly with the 2nd International Conference on Cloud and Green Computing (CGC2012). SCA2012 received 98 submissions from Germany, Canada, Japan, Australia, Sweden, South Korea, Portugal, Denmark, Poland and Mainland China. Each paper was peer reviewed by at least three program committee members. The final decision has been taken after a high quality review process. There are 45 paper accepted and the regular paper acceptance rate is about 32%. © 2012 IEEE.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.087 |
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".