Statistical engineering – Part 2: Future
Bibliographic record
Abstract
In the second of two panel discussion articles focused on the evolution of statistical engineering (SE) as introduced by Roger Hoerl and Ronald Snee, a group of leading applied statisticians from academia, industry, and government present their perspectives on what the future might hold for this important movement. The invited panelists discuss the challenges and opportunities presented by the emergence of data science and the abundance of large amounts of data. They also consider the possible paths forward for SE, and the roles for statisticians in academia, industry, and government. The final question addresses what additional skills would be helpful to increase the effectiveness of the practice and advance SE. As with the first article, the format of the article follows the order of a posed question, a summary of key ideas, and then the detailed individual panelist answers. The article seeks to inspire statisticians to consider their possible role to leverage the potential of SE to solve important problems.
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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".