Bibliographic record
Abstract
Columbia University School of the Arts’ Digital Storytelling Lab, in collaboration with Columbia’s Department of Narrative Medicine, developed Where There’s Smoke, a story and grief ritual that mixes interactive documentary, immersive theatre and online collaboration to invite healthcare providers and others into resonant conversations about life, loss and memory, and to imagine how stories can be used to create empathetic healing spaces. When Robert Weiler was diagnosed with terminal colon cancer, the complexity of healthcare and ensuing grief for the family, led his son Lance, a storytelling pioneer, to realize that a straightforward story wasn’t enough to explain and explore the experience, so he created Where There’s Smoke. Where There’s Smoke premiered in 2019 at the Tribeca Film Festival where it was hailed as an “absolute can’t miss” (Backstage). However, when COVID-19 submerged the world in loss, uncertainty, and isolation, Lance reimagined the piece as an online experience. He also combined the piece with protocols of Narrative Medicine as provided by faculty, Deborah Starr. The piece traces a heartbreaking journey through end-of-life care and grief, embracing grief as nonlinear and immersive, grief as an escape room with no escape. Participants sift through artwork, videos, and conversations and are provided with immersive moments for individuals, pairs and groups to have opportunities for self-discovery, unexpected intimacy, and ensuing healing. This is a personal yet universally relevant narrative, which gradually reveals itself to be something more…the possibility of immersive storytelling to create space for empathetic healing, grieving, and connecting.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".