IEEE/ACM ASONAM 2022: Message from the General Chairs
Bibliographic record
Abstract
The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.056 | 0.073 |
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".