Bibliographic record
Abstract
Abstract Posters sessions are popular informal forums to share emerging scientific results as they encourage discussion beyond results to include methodology, problems, and solutions. On the other hand, when there are too many posters, people suffer from cognitive overload and so are incapable of seeing and appreciating all of the research. Moreover, posters sessions are less well attended than presentations and as a consequence some authors leave the poster unattended. Poster competitions are one means to encourage presenters to stay at their post and people to come (especially when there are free refreshments). To identify the best poster(s) requires a simple scoring scheme, motivated judges who necessarily examine half‐a‐dozen or more posters, and a calibration method to account for differing opinions between judges. Judging is imperfect but we should be capable of agreeing which posters are among the top 10%.
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 | 0.121 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".