JS-CC: CONTROVERSIES IN ACADEMIA MINI-DEBATES
Bibliographic record
Abstract
Organized by the ISH Communications Committee, this session will feature two mini-debates focused on emerging controversies in academic/biomedical research. Our first debate will focus on the role of social media in an academic career and how this should be assessed in performance reviews. Dr. Anastasia Mihailidou (Australia) will argue the Pro perspective and Dr. Erika Jones (South Africa) will argue the Con. The second debate will focus on the increasing use of pre-prints in dissemination of research. Dr. Dylan Burger (Canada) will argue the Pro perspective while Dr. Swapnil Hiremath (Canada) will argue the Con. The session will emphasize effective communication in discussing these controversies and will feature a TED-style format and a more relaxed atmosphere than conventional debates. It will also include members of the Communications Committee as Panel discussants
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 | 0.067 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.075 | 0.024 |
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".