Bibliographic record
Abstract
While analyzing the transcripts of interviews with cancer survivors and writing up her findings, the first author found herself suffering from a writer's block of sorts. She was stuck, unable to move forward, encircled by a cloud of voices. Some voices came from the empirical data of the study, others emerged out of her private personal experiences, and one materialized from somewhere altogether more mysterious, urging her to bring the two together. This performance text aims to evoke that struggle to grapple with the many ‘ghosts’ that haunt our research (Doucet, 2008), while also working through the challenge of telling a ‘true’ cancer story (Park-Fuller, 2008). Interviews with 26 members of Gilda's Club of Greater Toronto (a meeting place for people affected by cancer) served as the data that was analyzed utilizing narrative analysis. Excerpts from the interviews were woven together to create the lines spoken by the character of the father. Three additional speaking parts were created—the daughter, the ghost, and the narrator—to help explore themes of isolation, navigation, and reflexivity. Thus, this is a story about feeling lost as a person living with cancer. But it is also a story about feeling lost as a researcher. Ultimately, it is the story of individuals struggling to make the pieces of their lives fit together, struggling to make their way forward, without always knowing how to do either.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".