Bibliographic record
Abstract
I was first introduced to the writing of Margaret Laurence many years ago, when her children's book The Olden Days Coat was published in 1979.Only eight years old, I was enthralled by the book and the protagonist Sal's travel back in time to meet her own grandmother at her age.What would it be like, I thought, to be able to do the same and meet my own grandma when she was my age?Would we be fast friends?It was the first time that I would read a book and feel that the place and characters in it were my own: it took place on the Canadian prairies, and the characters were people who lived there, just like I did.Only two years later, the Canadian Broadcasting Corporation (CBC) film version of the book was released, with Megan Follows, who would later play Anne in Anne of Green Gables, in the lead role of Sal.Watching the film on CBC television became a family tradition each Christmas season for many years to come.I remember those evenings, the fireplace crackling and the icy wind blowing outside, much like the cold winters described in the story.In early January of 1987, when I was sixteen years old, my mother sat the family down at the breakfast table and sadly and emotionally announced that Margaret Laurence had died.Profoundly influenced by her writing, she made it known to us how important Canadian literature was, and how significantly Margaret Laurence was a part of it.A few years later, I began to take English courses at the University of British Columbia, and for the first time to read Laurence's corpus of writing.Yet my early introductions to the author's work and her influence on my mother and me always stayed with me.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.511 | 0.328 |
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".