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Questioning the Morality and the Benefits  of Patenting Plant Life: Multinational Pharmaceutical Corporations and Intellectual Property Rights Laws            

2019· dataset· en· W2997262097 on OpenAlexaff
Erin MacDonald

Bibliographic record

VenueAuthorea · 2019
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsYork University
Fundersnot available
KeywordsMultinational corporationIntellectual propertyHumanityMoralityBusinessLaw and economicsCapitalismLawPolitical scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

In this paper, I set out to illustrate that international intellectual property rights laws are immoral because they allow pharmaceutical corporations to steal and patent plants native to developing countries. I also aim to show how the patenting of drugs creates monopolies that make it difficult for the world’s poorest to access certain drugs, and how these laws promote the destruction of the environment. Disappointingly, the organizations in which the majority of society trusts with conservation efforts help multinational corporations to gain access to plants native to the global South. In return, they get funds from corporations that are currently using biotechnology and want to be viewed as a force for good. Additionally, I give solutions to these problems throughout this paper, but stress that these solutions are within the global capitalist framework and not able to create structural change. The world is currently in crisis mode due to the blatant exploitation of the environment by powerful multinationals, and if capitalism is not changed in drastic ways, it is likely that humanity will be changed in irreparable ways, due to the devastating effects of climate change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.331
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.271
Teacher spread0.096 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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