The Fictions of Autonomous Invention: Accumulation by Dispossession, Commodification and Life Patents in Canada
Bibliographic record
Abstract
Abstract: In 2002 the Canadian Supreme Court ruled to deny Harvard College a whole organism patent over the oncomouse. In 2004, the same court ruled that Canadian farmer Percy Schmeiser had violated Monsanto patents covering GM canola. Both decisions rejected whole organism patents, running counter to US precedents. Yet both, nevertheless, consolidate private claims to life as patentable inventions, and critics claim, with some support from Justices in the Schmeiser case, that patents over genes amount to de facto patents over whole organisms. In this paper I argue these cases are broadly consistent with the notion of accumulation by dispossession as a means to expand the scale and scope of capital accumulation via so‐called ‘extra‐economic’ means. As such, I examine the cases as privatizations, but also as relational moments in the commodification of nature. However, in hoping to unpack and fill out this notion of the extra‐economic, as well as to critically examine the necessarily incomplete character of commodification as a tendency, I look to the ways in which judges and interested activists deliberate over the economic, legal, ecological, ethical, and even metaphysical arguments and representations required to uphold discrete genes, processes, and whole organisms as inventions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".