Dying.dialoguesJan 24 - Jan 25 2020‘Dying.dialogues’ is a symposium on design for end-of-life with collaborators from the Health Design Studio at OCADU Toronto and Taboo Health.
Bibliographic record
Abstract
Moth is a research project, which through the discipline of Graphic Design, helps with the unhiding of death. It aims to facilitate problem solving by making ‘tools’ to encourage dialogue about mortality, helping us to find perspective and turning death from something we fear into something we might learn from. Salkeld and Rudolph’s keynote starts Dying.dialogues, a one-day mini-symposium on design for end of life. Dying.dialogues will feature opening speaker Dr. Naheed Dosani Dr. Naheed Dosani is a palliative care and family physician at Inner City Health Associates and William Osler Health System. He is the founder and project lead of Palliative Education and Care for the Homeless (PEACH), a mobile, shelter-based outreach program that delivers palliative care for Toronto’s most vulnerable individuals. Dying. is an event series running in conjunction with DesignTO, with collaborators from the Health Design Studio at OCADU and Taboo Health.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.669 | 0.391 |
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".