Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The year 2008 marks the 100th anniversary of the publication of The Probable Error of a Mean by William Sealy Gosset, nom de plume “Student.” Gosset's work and his relationships with the leading statisticians of his day have been considered by several authorities. Despite the extensive documentation, and the seminal nature of the work, modern-day statistics textbooks give him, and this 1908 article, short shrift. Thus, few of today's students—or their teachers—are aware of the “z” statistic whose sampling distribution he actually derived, the mathematical derivation, his simulations to check his work, the material used in the simulations, the table he produced, the “one-line” missing proof supplied by the 22-year-old Fisher (still a student himself) or the subsequent switch, in collaboration with Fisher, from the z to the t statistic. We remind readers of these aspects, and rework his calculations using 21st century computing power. We hope that the next generation of statisticians come to know more about the man and his work than simply that “he worked for the Guinness brewery,” and appreciate that not all statistical distributions are derived in a single pass. Research students would do well to use his 1908 article as a model when writing their first statistical article.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it