Bibliographic record
Abstract
This is the first time the ASONAM conference has been organized in North America. I am so delighted to watch the conference moving up so fast with consistent and hopefully sustainable success. Achieving acceptance rate of 13% this year is a new record which brings a challenge for the new organizers. I am sure they are aware of the mission and will be able to maintain the same quality for the coming years. We need to show the success as permanent for ASONAM to continue its mission as the leading venue in the area of social networks analysis and mining. The number of quality submissions is rapidly increasing every year demonstrating the visibility of the conference and making it harder to select the papers to accommodate in the program. The quality of workshops co-located with ASONAM has been considerably improved this year. In addition, I am happy to witness the success of the two symposiums which have been integrated into the organization to have more specialized coverage of two key areas related to network based modeling and analysis. The symposium on the Foundations of Open Source Intelligence and Security Informatics (FOSINT-SI) is serving mostly researchers and practitioners interested in terror and criminal data analysis. On the other hand, the symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics (HI-BI-BI) covers the network applications in the health domain from the wet-lab to the clinic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".