Personal reflections on love, death and suffering: My narrative of the final days of my husband's life
Bibliographic record
Abstract
John was weak, something that had begun quite suddenly the day before, and he needed my help in order to lift himself off the couch or to climb the stairs or to maneuver throughout the house. That morning, I had to cancel an invitation to accompany my parents on a trip out of town; John was too weak for me to leave him alone. I had been looking forward to leaving behind my responsibilities for a bit ever since my parents had suggested that I needed a break—and the prospect of a short trip into the Ottawa Valley for a few hours, a chance to live a little out from under the weight of everything at home, was so enticing. The decision not to go was both completely inescapable and incredibly hard, and I sobbed for a long time after getting off the phone with my Dad. For the first time since John had become ill, I let myself tell him how unfair this disease was and how its presence in our lives was affecting not only him, but me—how I felt that I no longer existed in any meaningful sense apart from the illness, how my entire days were defined by the need to manage his illness, his almost daily trips to the hospital, the constant appointments, the endless challenge of trying to get some nutrition into him, the need to take care of everything around the house and yet to stay on top of work.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".