Bibliographic record
Abstract
While farmers set up conditions for the development of plants, the seeds they help grow into plants determine conditions for the farmers. Modern plants not only have agronomic characteristics but also intellectual property rights, phytosanitary regulations, and classifications attached to them. Interacting with their seeds creates fields of property and power, situations of possibility and impossibility, in which farmers and breeders operate. The biosocial networks from which seeds emerge are animated by bureaucratic measures, property relations, and research and cultivation practices that I will explore in action. Seeds not only become what they are in multifarious networks of natural, cultural, and political agencies, but their emergence and coevolution with humans is ruptured through deregistration, persecution, confiscation, and destruction of proprietary seeds. This article will take the reader from the fields of farmers in Saskatchewan to seed breeders in Saskatoon and ultimately to public meetings organized by the Canadian Food Inspection Agency in Ottawa.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".