Becoming a cancer survivor: An experiment in dialogical health research
Bibliographic record
Abstract
The article makes cancer survivorship the topic of an experiment in a form of writing we call dialogical response. First, in the style of autoethnography, each author presents an account of her or his long-term survivorship of cancer and the issues that involves. Less conventionally, we then respond each to the other’s story. The article seeks to contribute to an in-depth understanding of long-term cancer survivorship. More important, we offer it as an example of a form of writing rarely practiced in health research: speaking to those who participate in research, rather than speaking about those people. Among the multiple theoretical implications that could be explored, we consider Foucault’s concept of subjectification. Our argument is that recognising the discursive formulation of the subject can and should be complemented by recognition of the local, immediate dialogical formulation of subjects. Rather than presenting research findings about cancer survivors, we offer a performative enactment of survivorship as an ongoing process of dialogical exchange. We show ourselves, responding to each other, in the process of becoming the cancer survivors we are as a result of those responses.
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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".