Bibliographic record
Abstract
Many students are required to take one course in statistics, and many of those choose to take only one. This one course is often a “service” course, aimed at satisfying the perceived needs of various majors like social science or bioscience. The tradition in these courses is to include a fairly heavy dose of inference: confidence intervals, significance testing, p-values etc. After many years by many creative instructors of trying to make this an interesting and useful course, some degree of failure must be admitted. Perhaps it is time for a radical revision of the first course so that more students will take a second course. In this paper I will describe a way in which even the very first course can be made interesting and useful for students. Then I will suggest some further data analytic techniques that could be included in a second course. Both courses emphasize data analysis rather than the usual introductory inference procedures. The combination of simulation, resampling and graphical methods provide tools which allow instructors to describe variability and probability tools without involving mathematical development. While this approach will delay instruction of the more traditional inference material, it may be more useful than the traditional material for students who only take one or two statistics courses.
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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".