Dying. Using a public event series as a research tool to open communication on death and dying
Bibliographic record
Abstract
This paper will explore the use of public engagement as a strategy for encouraging and enhancing conversations about end of life through the variety of events that were part of the Dying., a public event series that ran in the 2019 DesignTO festival. Dying. invited practitioners, researchers, artists, and designers to collaborate with the wider community to explore the topic of death and dying. The Dying. series attracted over 4,000 attendees in 2019, 14 speakers, and 12 exhibiting artists. These events included public engagement through interactive exhibit, a public art/design show, public lectures, participatory art installations, participatory design workshops, and evidence-based game playing. Dying. encouraged dialogue among community members and practitioners, initiating non-medical portrayals and expression of experiences associated with dying and death. Part, research tool for knowledge mobilisation, the interactive exhibits served to engage the public in sharing experiences of end of life in light weight and playful interactions, as well as more heavy weight interactions. Data gathering for research on health topics using participatory public exhibit was part of the research intention behind the design of the exhibits. Dying. opened an interdisciplinary dialogue between designers, medical practitioners, and the public, addressing a need among practitioners for more opportunities to share their work and learn from colleagues, and a need among the public for opportunities to hear and experience a more varied discourse about death (knowledge mobilization). Dying. creatively offered the public multiple ways to engage with the topic of end of life also supplying supporting resources on advanced care planning and other aspects of end of life decision making.
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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".