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Record W7002107335

Of Lock-Breaking and Stock Taking: IP, Climate Change and the Right to Repair in Canada

2023· article· en· W7002107335 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2023
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeStock (firearms)Order (exchange)Intellectual propertyCentralityGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

This paper argues that Canadian governments have both legal and moral obligations to act to combat climate change. In seeking to fulfill these obligations, Canadian governments should pay particular attention to Canada’s intellectual property (IP) regime. This paper argues that given the centrality of IP to Canada’s economy, a comprehensive review is required in order to determine whether and the extent to which elements of Canada’s IP regime contribute to climate change or impede climate action. To illustrate the need for such a review, this paper will highlight one example of how Canada’s IP regime, as currently structured, impedes the fight against climate change. Specifically, it will focus on the provisions of Canada’s Copyright Act that provide protection for technological protection measures (TPM). These provisions limit the extent to which consumers can repair software-enabled products that they have purchased. Reform of the TPM provisions in Canada’s Copyright Act is required in order to ensure that they do not act as a barrier to repair. This paper will discuss several options for reform. While a comprehensive review of Canada’s IP laws is necessary in order to identify and amend all provisions that contribute to climate change or that impede climate action, amending the TPM provisions of the Copyright Act to include an exception for the purposes of diagnosis, repair, and maintenance would be an important step in this direction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.245
Teacher spread0.221 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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