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Record W2023404537 · doi:10.1136/bmj.h1369

European Commission's proposals on trade secrets

2015· editorial· en· W2023404537 on OpenAlexaff
Martin McKee, Ronald Labonté

Bibliographic record

VenueBMJ · 2015
Typeeditorial
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual propertyJurisdictionParliamentInternational tradeCommissionMultinational corporationLegislationBusinessNegotiationLaw and economicsCorporationTRIPS AgreementEconomicsLawPolitical scienceFinancePolitics

Abstract

fetched live from OpenAlex

Risk undermining public health and must be modified In late 2013 the European Commission published proposals to harmonise elements of existing national legislation on trade secrets.1 These will shortly be debated in the European parliament but, in their present form, they have created serious concerns among non-governmental organisations concerned with health policy.2 Strengthening protection against disclosure of trade secrets is the most recent step in a process whereby multinational corporations have increasingly sought to commodify knowledge. Thus, the drug industry has lobbied to strengthen the protection given to it by the patent system—for example, by persuading governments to increase the duration of protection for so called orphan drugs3 and using international trade negotiations to enable it to claim rights in previously unprotected markets such as India. A diverse range of industries has exploited the opportunities provided by transfer pricing, whereby operations selling a trademarked commodity in one country pay large sums to another part of the same corporation based in a low tax jurisdiction for the right to use the brand name and associated imagery.4 The arguments in favour of such arrangements are well rehearsed. Patent law gives corporations rights over intellectual property …

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.009

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 head0.130
GPT teacher head0.279
Teacher spread0.149 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations2
Published2015
Admission routes1
Has abstractyes

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