Bibliographic record
Abstract
The ACM Multimedia conference just celebrated its quarter century in October 2017. This is a great opportunity to reflect on the intellectual influence of the conference, and the SIGMM community in general. The progress on big scholarly data allows us to make this task analytical. I download a data dump from Microsoft Academic Graph (MAG) in February 2016. I find all papers from ACM Multimedia (MM), the SIGMM flagship conference - there are 4,346 publication entries from 1993 to 2015. I then search the entire MAG for: (1) any paper that appears in the reference list of these MM papers - 35,829 entries across 1,560 publication venues (including both journals and conferences), with an average of 8.24 per paper; (2) any paper that cites any of these MM papers - 46826 citations from 1694 publication venues, with an average of 10.77 citations per paper. This data allows us to profile the incoming (references) and outgoing (citations) influence in the community in detail.
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 | 0.040 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.041 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.054 | 0.048 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.058 | 0.031 |
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".