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Putting Intellectual Property in its Place

2014· book· en· W4234379997 on OpenAlexaff
Laura J. Murray, S. Tina Piper, Kirsty Robertson

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsWestern UniversityMcGill UniversityQueen's University
Fundersnot available
KeywordsIntellectual propertyCreativityScholarshipPremiseContext (archaeology)NormativeNegotiationSociologyPolitical scienceJournalismLegal practiceLawEpistemologyHistory

Abstract

fetched live from OpenAlex

Abstract This book examines the relationship between creativity and intellectual property law on the premise that, despite concentrated critical attention devoted to IP law from academic, policy, and activist quarters, its role as a determinant of creative activity is overstated. The effects of IP rights or law are usually more unpredictable, non-linear, or illusory than is often presumed. Through a series of case studies focusing on nineteenth-century journalism, “fake” art, plant hormone research between the wars, online knitting communities, creativity in small cities, and legal practice, the book discusses the many ways people comprehend the law through information and opinions gathered from friends, strangers, co-workers, and the media. It also shows how people choose to share, create, negotiate, and dispute based on what seems fair, just, or necessary, in the context of how their community functions in that moment, while ignoring or reimagining legal mechanisms. The book defines “the everyday life of IP law,” constituting an experiment in non-normative legal scholarship, and in building theory from material and located practice.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.020
Scholarly communication0.0130.011
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0170.004

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 head0.049
GPT teacher head0.286
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations33
Published2014
Admission routes1
Has abstractyes

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