Micro-Life: A Reflection on Species and Collaboration
Bibliographic record
Abstract
Micro-life is a heuristic for researching and thinking across forms of life and matter at a very minute scale without exceptionalizing any one micro-organism in particular. It draws out the fraught relationship between discourses about species and the matter of the species itself. Focusing on micro-life helps name political orientations to the world, processes of disidentifying with the coherence of our material selves, and experiences that invent and sustain collaborative and fluid relations. Micro-life was generated during a performance studies conference. Thus, as a performance studies methodology, it helps foreground the non-visual exchanges that occur during performance. Micro-life makes room for reflection on the politics and possibility of human (and non-human) collaboration, but, as something to think, live, and make politics with outside of performance studies, it is a category of life and analysis for what we call the cellular humanities.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.108 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".