Bibliographic record
Abstract
The phone rings in late evening. “Hello? Is this Jennifer? Your mother wants to talk. She needs you.” “Jennifer dear? I'm lost. Maybe I'm on Sherbrooke Street.” As I go to her rescue, I think of Mom during the war. Living in the country, away from the threat of bombs. Chopping wood for heat, for cooking. Snowshoeing to the neighbour's over snow-clogged roads. The phone lines down again. Knitting socks, ... and socks, ... and socks — for the soldiers. Playing the piano. “My Bonnie Lies Over the Ocean.” No soap. No butter. No new clothes. No car. Her brothers and sister in far-off cities. Chatting on the party line just to hear an adult voice. How did they do it, those women of the war? Waiting five years, never knowing, making all the decisions. Now her mind has softened. A gentleness shines through. And bewilderment. Sometimes anger. “Look at the way that nurse dressed me! Nothing matches. She just throws the clothes at me.” Dementia, creeping up on her as she fights to hang on. Her friend Bessie dials the number taped to the phone. “Please come. Your mother is frightened. Will you come? She's lost.” I hurry down the hall while they peek at me sideways; Mom in her wheelchair, Bessie at her side. Sentries at the nurses' station. Waiting for life to happen. Waiting for Jennifer. Holding hands. Bessie's hand strokes my Mom's, reassuring, firm, gentle. “There, I told you she'd come!” “Hello, Bessie. Are you the friend who helped Mom? Are you the one who called me?” Bessie looks inquiringly at the nurse. “Was I the one who called Jennifer?” “Yes, Bessie.” Bessie has forgotten. But her heart knew to be kind. She wanders off, already putting it all behind her. I bend to kiss Mom's soft cheek. She asks timidly, “Are you my mother?” Damned dementia. Jennifer Raiche CMAJ
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.819 | 0.645 |
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".