Multiform Narratives in the Heterotopic Space of Technoscience
Bibliographic record
Abstract
Broadly speaking, this thesis explores the practices used by employees of a Western Canadian genomics research facility to build four-dimensional models of the human body; models which will be used to study genetic diseases. Usiiig actor-network theory as both a theoretical and methodological foundation, I consider the ways in which both the social (human) and the technical (nonhuman) actors that comprise the genomics research facility work together to construct these models. The work is divided into two sections. First, I investigate the setting of the genomics research facility. I argue that the genomics research facility constitutes a heterotopic site of cultural production. Second, I question what the genomics research facility produces. Ultimately I argue that by using fully immersive virtual environments, and building generic and extendible virtual models of the human body, employees at the genomics research facility are able to produce complex, multiform narratives of biological processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".