Bibliographic record
Abstract
MY FIRST RECOLLECTION of Joanne is as an undergraduate taking a summer course taught by her at St. Michael's College in the 1970s.I had heard so much about her, and when I took one of her courses, it was clear that the fame was well deserved.At that time there were very few women teaching in Religious Studies, and we were in awe.Years passed before I would have the chance to get to know her personally, but along the way her reputation grew and grew.The great thing about her lectures was that she never relied on her personality or special effects to catch the classes' interest.It was her dedication as a scholar that caught the attention and won the respect of one and all.Even the most conservative Anglican bishops listened to her, and her address at the General Synod, when the Anglican Church of Canada voted to ordain women to the priesthood, most certainly influenced the vote in favour.Remember that she was speaking as a Roman Catholic laywoman at the time.Subsequently I remember the buzz of excitement when word spread that Dr. Joanne McWilliam had been received into the Anglican fold and would be teaching at Trinity College, Toronto.There was further excitement when we heard she was applying for holy orders.It fell to a group of Examining Chaplains in Toronto to interview her, and I remember well the slightly nervous question: "Which one of us is going to quiz her on theology?"Needless to say, the theological questions were handled beautifully.No surprise there, but afterwards I remember all commented that prayer vii
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.469 | 0.369 |
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".