Bibliographic record
Abstract
Driven by our mission, we have advanced science for the public good. Our history is rich in theory, methodology, and applications that are instrumental in solving real-world problems. Our reach is boundless, and our impact is undeniable in diverse areas, including engineering, economics, the environment, genetics, public health, and health policy. As the data landscape continues to evolve, our leadership, expertise and knowledge will be essential to meet the global challenges of the future. We must boldly embody our mission of promoting the practice and profession of statistics. In this talk, I will highlight our mission in action as we strive to be a community that uses statistical thinking to drive discovery and inform decisions. This article summarizes the 2023 presidential speech given at the JSM in Toronto.
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 | 0.066 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".