Bibliographic record
Abstract
How do we see life after the century of the gene? This article argues that the post-2000 postgenomic turn was and is a thoroughly visual turn, as well as a theoretical and practical shift away from the central dogma of DNA as master molecule. Live-cell imaging is a rapidly expanding area of scientific visualization of living things whose practice is central in postgenomic biological research and theory. Fluorescent probes enable the visualization of the movement in vivo, over time, of a wide range of vital molecules, for example the movement of motor proteins along the cellular skeleton. Despite its prominence in the life sciences, these moving images have attracted little critical attention outside the scientific community. Comparison with microcinematography of the early 20th century, another time-based medium that also placed the capture of movement at the center of the technique, is used here to frame the emergence of live-cell imaging in the late 20th century and discuss its theoretical significance. This article argues that live-cell imaging was at its origins an animation of a theory of life dominated by the gene. However, focused as it is on the life of proteins, the practice actually facilitated a move away from such dominance, with a rise of a ‘molecular vitalism’: an interest in all cellular molecules as knitted together in a complex moving net in the time and space of the cell. As such, the present moment echoes early 20th-century tensions between the study of structure and function in cellular anatomy versus physiology and puts the focus on molecular movement just as cellular movement was central to earlier practices. Contemporary live-cell imaging does not depict a structure described in a unique moment that explains a life process, but rather visualizes a continuity of movement that constitutes life processes.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".