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Record W2057961111 · doi:10.1353/lm.2012.0017

Cancer Experience and its Narration: An Accidental Study

2012· article· en· W2057961111 on OpenAlexaffabout
Judy Z. Segal

Bibliographic record

VenueLiterature and medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarrativeAppropriationCancerConversationNewspaperMedia studiesHistoryMedicineSociologyLiteratureArtPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Cancer Experience and its Narration:An Accidental Study Judy Z. Segal (bio) Introduction Literature and Medicine has been in the vanguard of publication on illness narratives in general and cancer narratives in particular. As a contribution to the conversation in the journal, I would like to describe research I have done recently on how the most popular and most available stories of cancer are received by people with cancer and those close to them. My "data" was collected recently, during the course of my own radiation treatments for breast cancer; I will soon explain why I have placed "data" in quotation marks. Many illness stories circulate in public and they can do important public work. I wish, keeping in mind claims made by Arthur Frank about the importance of illness stories "in an age of authenticity and appropriation" (the subtitle of his essay), to look at certain illness stories critically.1 In this endeavor, I am indebted to a number of other authors, many of whom I will cite in what follows, and—this is the "data"—a corpus of reader emails sent to me in response to an op-ed I wrote for my local newspaper, the Vancouver Sun, at the beginning of Cancer Awareness Month, April, 2010. In that piece, I wrote about popular cancer stories and the expectations they create; I shared some of my own cancer experience; and I unintentionally prompted the archive on which I am reporting here. I understand that my data, because it simply appeared, unbidden, not under experimental conditions, is called, "wild data." The op-ed was called "Cancer Isn't the Best Thing that Ever Happened to Me," and it is appended to this essay. [End Page 292] Background The title of my op-ed made a true claim about my cancer experience. The claim should have been unsurprising, but it was a counterclaim in the context of current public discourse, despite the fact that my protestation was in harmony with much that has been written in breast-cancer critical work over the past 30 years and more. In 1980, Audre Lorde wrote, speaking of breast cancer, that, "[l]ike superficial spirituality, looking on the bright side of things is a euphemism used for obscuring certain realities of life, the open consideration of which might prove threatening to the status quo."2 In 1994, Sharon Batt, writing on the politics of breast cancer, said that the "Cancer is the best thing that ever happened to me" narrative might capture "one side of breast cancer—the need for hope," but it leaves a lot unsaid.3 In 1997, Jackie Stacey wrote about the problems of viewing cancer narratives as stories that turned people with cancer into heroes.4 Ten years later, S. Lochlann Jain wrote that oft-told breast-cancer narratives "pinkwash the experience of the disease, diffusing other kinds of emotion, making them illegitimate, or worse, making them into something illegible."5 While, over the course of the years of this critique, we have seen some changes in the public account of the cancer experience—notably, the growing influence of Breast Cancer Action (about which more later)6—many still seek the "bright side" of breast cancer; "hope" is still the keyword in cancer talk; the hero narrative is still dominant, and the dominant narrative is still pink. Diane Price Herndl, explaining her own ambivalence about certain personal cancer narratives, notes that one of the things that makes her uneasy about the most publicly-circulating, triumphal, stories of breast cancer is their coda, written or unwritten, that says, "Be like me."7 I share the concerns of Lorde, Batt, Stacey, and Jain, and I think Herndl is right, and I would point to another cancer-story coda that says (like a mean girl), "I'm so great." This is the way the standard narrative goes: "I found a lump; I was scared, but I fought; with my positive attitude, I beat cancer; now I'm a better person. (I'm so great.)" It is worth noting that cancer narratives do so often begin with a discovered lump or a worrisome mammogram, when, in fact, a cancer's own biography has a...

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.387
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2012
Admission routes2
Has abstractyes

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