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Record W3111654361 · doi:10.1163/18253911-03503001

Things That Don’t Talk Much and Things That Feel

2020· article· en· W3111654361 on OpenAlex
Elizabeth Neswald

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNuncius · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsBrock University
Fundersnot available
KeywordsEmbodied cognitionObject (grammar)Intersection (aeronautics)Boundary objectTacit knowledgeAestheticsComputer scienceEpistemologyHealth careHuman–computer interactionSociologyKnowledge managementArtificial intelligenceSocial scienceArtEngineeringPhilosophyPolitical scienceNegotiation

Abstract

fetched live from OpenAlex

Abstract This essay explores whether and how objects that seem pedestrian and anonymous can be made fruitful for material culture study. Using the example of late 20th-century blood glucose monitors for diabetes, it assesses the potential and limitations of common approaches to the study of material objects, when the object itself is unremarkable. It then turns to object- and experiment-oriented work in the history of science and seeks to integrate the concepts of tacit and embodied knowledge to formulate an approach to medical objects based on bodies and practices. Comparisons between monitors show how changes in their material configuration affected these practices and, by extension, changed the relationship between user and object. Finally, it looks to studies on the objects and practices of medicine and healthcare at the intersection of bodies and emotions and asks what insights they can provide for the study of modern medical devises.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.299
Teacher spread0.246 · 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