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Record W4366977217 · doi:10.1111/maq.12754

Beyond the Limits: Conversation, Part IV

2023· article· en· W4366977217 on OpenAlexaff
Laura A. Meek, Abigail H. Neely, Tatiana Chudakova, Sienna R. Craig, Casey Golomski

Bibliographic record

VenueMedical Anthropology Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsConversationCitationLibrary scienceAnthropologySociologyComputer science

Abstract

fetched live from OpenAlex

Sienna Craig:As we talk, I keep returning to the specificity of moments in ethnographic practice.I keep thinking about the phrase, "Follow the money."What I mean by this is: How and in what ways do people's ontological commitments to worlds that include ancestors, or particular deities, or ways of rectifying relationships with sacred geographies, connect or relate to larger political-economic questions about who has what kinds of resources-literally money in some cases and other forms of capital in others-to act on illness in those moments?And how does that relate to healing outcomes?Laura Meek: Maybe one way that we are all combining these ontological and critical approaches in our pieces here is through prioritizing the everyday, its quotidian rhythms, intimate relations, and nonhuman actors.I wonder if situating our work in the mundane helps reveal how the limits of health and medicine are imposed and challenged through even the smallest acts of everyday life?I'm thinking of moments in these pieces like sipping tea, applying for IRB approval, offering incense, tasting a pill … Casey Golomski: It's also about what you foreground in your writing: Are you foregrounding somebody's words?Or are you foregrounding relationships between their words and other things in the environment that they may or may not comment upon, but that you think you see as an anthropologist or analyst?What is the locus of the interpretive gaze, where does "the human" sit in this gaze, and how have these been constituted in writing?Sienna Craig: This is part of what I think is both exciting and sometimes confusing about being situated within medical anthropology.On the one hand, the subdiscipline has such breadth; on the other, it is still figuring out how to marry the critical with the speculative.In my piece, I was trying to think and 110

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)0.019
metaresearch head score (Gemma)0.046
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0360.046
Scholarly communication0.0170.021
Open science0.0030.015
Research integrity0.0130.027
Insufficient payload (model declined to judge)0.0100.003

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 head0.028
GPT teacher head0.356
Teacher spread0.328 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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