Bibliographic record
Abstract
I tell my story through the lens of an older woman who is a mother of three, a grandmother of eight, an occupational therapist by training, an academic, the wife of a prominent academic, and Jewish.What complicated my life growing up in Toronto in the 1940s and '50s is the fact that my mother died just after I turned ten.My story begins where my mother's story ends.I write about her and the bits I know about my life before she died.I write about her death, how we managed, and how she remains a presence in our lives.And I write about the rest of my life.In the years that followed my mother's death, my father took on the task of parenting my brother and me.My mother's large family stayed close, as did my parents' friends.Jewish family life made for another layer of protection from my disrupted sense of belonging.I learned to pick up and move on, not in an unfeeling way, but simply as a way of coping.Over time, I must have learned that there was a way to persevere, to fashion darkness and create light. 1 When I was a child and an adolescent, my school life and my social life were happy; I felt loved and cared for at home, and I developed a good sense of self.Toward the end of my teenage years, I, like most young women in my time and place, had put getting married at the top of my to-do list.I not only managed to achieve that goal, but I did so early and well.I was engaged by the time I entered the University of Toronto and married a year later, in 1958, at the tender age of nineteen.I continued with my course in physical and occupational therapy and my husband finished his program in law.After graduating in 1960, we moved to Cambridge, England, for a year for my husband to do graduate work.I worked as an occupational therapist in a psychiatric hospital in Cambridge, and then in Toronto, before starting a family.x Prelude
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.368 | 0.230 |
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".