Bibliographic record
Abstract
Like many women of my generation, I grew up with stories; lots of them, and all kinds.Some were nonsensical, others were riddles.There were ahtyokaywina, the sacred stories, and others that were tahp acimowina, the family histories.But it was the stories about the women that I loved best, stories about strong, courageous women and about weak, foolish ones, stories of births and deaths and women's cycles; of bad love affairs, brutal husbands, and yes, strong and gentle men.Some of the stories were told while we worked, cutting up meat and packing jars for canning or while smoking and drying fish.Some were told on the way to the berry patch."Look, that's the place where your cousin Jenny was born," the story went."Your auntie was craving Saskatoon berries, but she was told by her mother and the old midwives not to go berry picking because she was almost ready to deliver her first baby. of course she didn't listen, the mut sti quon that she was, she snuck away.Well wouldn't you know it, another mut sti quon, a young black bear, had slipped away from her mother and, like your auntie Betsy, was gobbling berries as fast as she could.When your auntie Betsy came around the bush she bumped right into her.They both screamed and ran.I don't know what happened to the young black bear, but your auntie Betsy tripped and fell and, not long after, Jenny arrived.Good thing Betsy had a good set of lungs.Her mother said she was screaming so loud her voice echoed all over the valley.It was hard to know if one woman was having a baby or ten.Yes, this is Jenny's berry patch."our nohkoms would laugh whenever they stopped at the place where they found Betsy.To this very day, that particular place on the side of the hill is still called "Jenny's berry patch."It is one of the family and landscape stories of our area.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.823 | 0.808 |
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".