Bibliographic record
Abstract
On one of my family's regular visits to Oshawa when I was a child, my grandmother asked me to run upstairs and fetch a sweater from her dresser drawer.In doing so, I came across a bundle of letters tied together with a ribbon.Even now, I remember feeling somewhat guilty for peeking and seeing the word "Sweetheart."They turned out to be Grandpa's letters home during the First World War, carefully preserved by Grandma.Grandpa did not speak of the war very much: he would just say that it was "all a long time ago."A few years after Grandma died, my grandfather decided to move into a retirement home in Oshawa to be with friends.Grandpa was quite wholesale in discarding possessions in preparing for the move and would likely have destroyed the letters.Fortunately, they were saved once again, by my mother, his eldest daughter, Winnie.Many years later, when the time came for my mother to move into a nursing home, I asked her to pass the letters on to me, which she did.By then I had read beyond "Sweetheart" and had resolved that Grandpa Timmins' letters would have further adventures.I wish them well.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.445 | 0.374 |
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".