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Record W2782093713 · doi:10.1177/1746847717729594

Animating Molecular Life: An Interview with Natasha Myers

2017· article· en· W2782093713 on OpenAlex

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

VenueAnimation · 2017
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsYork University
Fundersnot available
KeywordsEmbodied cognitionAnimationStyle (visual arts)EthnographyComputer scienceCognitive scienceMechanism (biology)Human–computer interactionSociologyAestheticsVisual artsEpistemologyPsychologyArtComputer graphics (images)PhilosophyArtificial intelligenceAnthropology

Abstract

fetched live from OpenAlex

In this interview, conducted by special issue co-editor Joel McKim, anthropologist Natasha Myers discusses her ethnographic exploration of how protein modellers attempt to render visible the nano-scale molecular structures that make up cellular life. Myers reflects on the ways these scientists make use of computer animation and other forms of embodied knowledge (including movement) as essential tools that allow them ‘to see beyond the limits of vision’. McKim and Myers discuss the tensions that arise when the goal of scientific accuracy meets the forms of aesthetics and style intrinsic to these activities of modelling. Myers identifies the ‘lively mechanism’ involved in the animated machines generated by the molecular scientists she observes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.132
GPT teacher head0.438
Teacher spread0.306 · 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