Bibliographic record
Abstract
L ike many other people in Saskatchewan, I first heard of Florence James from my high school drama teacher, who was fresh from a class that Florence and her husband, Burton (better known as "Pop"), had conducted in 1951 for the Saskatchewan Arts Board's Summer School.Burton died on November 13, 1951, and in 1952 Florence returned to teach summer classes in Saskatchewan by herself.That summer, I signed up for her two-week session in the Qu'Appelle Valley.As a bright-eyed wannabe actor, I was mightily impressed by this forceful, knowledgeable woman.I returned to take her classes for several summers after that, even after I had graduated and moved on into community theatre.Mrs. James (we always called her "Mrs.James" in those days) was a touch formidable-you didn't fool around in her classes.But she knew her stuff.We respected her, and we learned from her.Many years later, in her papers, I found the letter from Arts Board Secretary Norah McCullough inviting Florence to make her connection with the board a permanent one.The letter was dated September 11, 1952, and said that while Norah and Executive Director David Smith hadn't yet been able to try out the idea on Dr. William Riddell, chairman of the board, "we are afraid you will escape us, so this is to fists upon a starx
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.420 | 0.219 |
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".