Bibliographic record
Abstract
: Our mum was diagnosed, declined and died over a period of two months and one week. My siblings gathered from around the globe. We cared for her in her home on a small island off the west coast of British Columbia (Canada). She described this as the richest period of her life. We shared the care. And her friends, "the walkie-talkies" came to support us and to check in on her. We took time for fresh air. We walked the beaches. A piece of driftwood inspired my brothers to build a coffin. Silk in her studio inspired my sister to create a beautiful shroud. We snuggled with her, talked, sang, reminisced. We listened to her stories. And then in the quiet of the night, she died. We kept her body at home for a full day. Her friends gathered. Then early the next morning, with government permit to transport her body, we went via ferry, and drove her down Vancouver Island, past the green burial ground that my brother designed to the crematorium. The next day the bereavement counsellor greeted us, and with warmth and sensitivity introduced us to the staff and the cremator. After a bit of time, we lifted her body into the cremator and pushed the button. In Canada, the majority of after death care is provided by funeral professionals, but there is a growing interest in the concept of do it yourself care for the body and funerals. The purpose of this presentation is to share a photo journal of this experience, and depending on time allotted, open time for discussion regarding "do it yourself" care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".