Bibliographic record
Abstract
The COVID-19 pandemic forced the organizers of the IEEE/ACM conference on advances in social network analysis and mining (ASONAM) to go for virtual conference two years 2020 and 2021. It was not easy to handle the program over these two years because the speakers were widespread in all parts of the world with different time zones. It was not possible to get everyone together at the same time. For some sessions speakers were coming in to present then leaving to go back to sleep because they were forced to wake up and present even after mid-night in their time zone. All of this convinced the organizers that virtual conferences will not work for a real international conference; it might be acceptable for local or regional conferences. However, even by passing the time zone problem will not help solving the major problem or socialization and discussion beyond what is presented at the conferences. This will not be effective without in person attending. Realizing this need, we decided to move back to face-to-face in person participation in 2022. However, the unclear situation of COVID-19 in early 2022 did not help us to go all in person. To stay on the safe side, we decided to go hybrid for 2022 with in person component hosted by Istanbul Medipol University. Only after we completed the conference, we realized that hybrid is the option which should be avoided. It is hard to satisfy two groups of participants simultaneously. The local organizers end up investing their time and effort for lower number of in-person participants. This uncertainty in the organization modality has influenced the number of submissions which decreased compared to the previous years. However, the organizers decided to reduce the size of the conference in 2022 to avoid sacrificing the quality, keeping acceptance rate below 20%. This helped maintaining the acceptance rate which has stabilized around 13-18% since ASONAM was organized in Istanbul, Turkey in 2012. Indeed, Athens was the city where ASONAM was born in 2009 and Istanbul was the city where ASONAM in 2012 showed first signals of maturity and stability in terms of the number of submissions, acceptance rate and participation; the same high quality has been enforced again in Istanbul in 2022. We are happy to see the stability sustained and ASONAM kept its permanent position among top tier international conferences. Every year, authors of all papers presented at ASONAM, and the co-located events are invited to submit expanded versions of their manuscripts to the prestigious SNAM journal, NetMAHIB journal, or the LNSN series which are characterized by their high visibility and fast processing of submissions. Special thanks to Springer Nature for their continuous support since ASONAM started in 2009 and for having their prestigious venues which have been well integrated with ASONAM to the benefit of both parties.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.097 | 0.150 |
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".