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The Fictions of Autonomous Invention: Accumulation by Dispossession, Commodification and Life Patents in Canada

2007· article· en· W2111810628 on OpenAlexaffabout
Scott Prudham

Bibliographic record

VenueAntipode · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsCommodificationSupreme courtLaw and economicsDe factoSociologyCapital (architecture)LawScope (computer science)EconomicsPolitical scienceMarket economyHistory

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.992

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0280.025
Scholarly communication0.0110.003
Open science0.0020.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.025
GPT teacher head0.230
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations153
Published2007
Admission routes2
Has abstractyes

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