Bibliographic record
Abstract
<h2>Highlights:</h3> Add .delete_topics by @shuanglovesdata in #2322 Allow execution without plotly by @luismavs in #2401 Add tqdm to _litellm.py @NFrnk in #2408 Drop support for python 3.9 by @afuetterer in #2419 Make UMAP's init default to random on visualize_topics for reproducible visualization by @makramab in #2412 <h2>cuML:</h2> Preparing for MEGA!-scale BERTopic with Multi-GPU UMAP and the following necessary updates: Update installation instructions for cuML with BERTopic by @csadorf in #2446 Speed up ._create_topic_vectors by replacing DataFrame .loc with NumPy masking @jinsolp in #2406 Modify _reduce_dimensionality to use fit_transform by @betatim in #2416 <h2>Fixes:</h2> Fix incorrect label in zero-shot svg in documentation by @huisman in #2448 Enable ruff rule RUF by @afuetterer in #2457 CI: bump github actions versions by @afuetterer in #2427 CI: prefer action-pre-commit-uv for lint job by @afuetterer in #2434 CI: switch to uv based project installation by @afuetterer in #2445 Chore: update pre-commit hooks by @afuetterer in #2414 and #2443 Chore: remove obsolete version_info check by @afuetterer in #2444
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.509 | 0.578 |
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".