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I Have Many Sick Hearts

2023· book-chapter· en· W4377037319 on OpenAlexaboutno aff
Jens Brockmeier, Maria I. Medved

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeIndigenousHybridityMeaning (existential)SociologySociology of health and illnessAestheticsGender studiesPsychologyAnthropologyPsychotherapistLiteratureHealth carePolitical scienceArt

Abstract

fetched live from OpenAlex

Abstract We live in cultural worlds abounding with stories of health and sickness and many shades in between. This chapter makes the case that it often is difficult, if not impossible, to clearly distinguish between illness narratives and narratives that tell how illness is interwoven with life and its challenges. Whatever else illness is, it is always interwoven with people’s personal experiences, evaluations, and emotions. It is endowed with meaning and sense making that cannot be distinguished from the significance illness has within a given culture. Drawing on resources from narrative psychology, narrative medicine, cultural psychology, cultural anthropology, and critical health studies, the authors elaborate this point in studying the stories told to them in interviews with Canadian Indigenous women who, living on reserve, suffer from heart disease. In particular they look at one multilayered narrative that gives a drastic example of the well-known cultural hybridity of life lived on indigenous reserve within a settler culture.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.006
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0520.015

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 head0.038
GPT teacher head0.312
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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