Bibliographic record
Abstract
Essential to the writing of this history was the 100th Anniversary Book Committee, which not only encouraged my journey into The Study world but also generously provided insights at every stage.Specifically, through Eve Marshall and Mary Liistro Hébert I was drawn into the complex world of a headmistress, and by Jill de Villafranca, into Study parents, and the workings of the Study board.Susan Orr-Mongeau tirelessly supported the project at every step of the way with her excellent administrative skills and her knowledge of The Study community.Belinda Hummel, with her amazing organizational skills, continuously supported the project, and with good-natured tolerance of the disorder involved in writing a history such as this in a very short time.Pattie Edwards was always ready to share her extensive knowledge of Study Old Girls, with good humour.Ellen Yambouranis at the reception desk promptly and at a moment's notice arranged working space.Always smiling.Susan Papini unearthed a few rare and important documents.Amalia Liogas and Mary Milligan enriched the project by generously sharing many of their photographs.
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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.321 | 0.193 |
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".