Bibliographic record
Abstract
I would like to dedicate this book to Joany, my lifelong friend and tennis partner and my wife of fifty years.We had the extraordinary good luck to meet in Cap-a-L'Aigle, where our families spent their summers, when I was six and she was five.I had befriended a boy named Ted Robb, who was visiting his relations, and Joany was one of his cousins.The recollection of that first meeting, which took place at the Manoir swimming pool, is vivid to me and very vague to her.We met again on the tennis courts of the Murray Bay Golf Club seven years later, when we began a tennis partnership that grew into a decades-long romance.In those days, while our early common interest was tennis, Joany was also a good downhill skier.She seduced me up the hills of the Laurentians and taught me how to get to the bottom, albeit without her grace and speed.After graduating from Kings Hall Compton, Joany entered the Montreal General Hospital School of Nursing in 1952..She was following in the footsteps of her grandmother, Isabelle Hampton Robb, who had been the first director of nursing of the John Hopkins School in Baltimore and was the founder of the American Journal of Nursing and of several nursing associations.A very good student, a sympathetic listener, a gardener with an extraordinary green thumb and an enthusiastic cheerleader for her family and friends, Joany has been an inspiration and model for us all.On the occasion of Joany's seventieth birthday, Janyne Hodder, at the time the principal of Bishop's University, wrote the following to her on behalf of the university: You have the great gift of making everyone feel special, of always finding the right words and of warming the day of everyone who meets you.Kind and funny, gentle and wise, Bishop's First Lady is first in our hearts.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.221 | 0.201 |
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".