MétaCan
Menu
← Back to cohort
Record W7010748815

Investigating the causes and effects of IP contract strain

2010· dissertation· en· W7010748815 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
FundersAdvanced Foods and Materials Network
KeywordsIP address managementIntellectual propertyContract managementNegotiationCore (optical fiber)
DOInot available

Abstract

fetched live from OpenAlex

Currently private sector global markets are driven by sharing and trading of intellectual property (“IP”) assets, as facilitated by IP contracts. Experts recognize that IP contracts can be problematic. Prior literature analyzed problems in IP contracts through a lens of legal rules. Adopting a wider vantage than the prior literature, this thesis examines the role of IP assets in IP contracts and identifies the problems in this relationship as IP contract strain. Specifically, this thesis queries: What are the causes and effects of IP contract strain? IP contract strain has several causes including contractual terms failing to recognize the IP distinguishing traits of an IP asset. This set of traits provides a base definitional framework, applicable to an analysis of IP assets in IP contracts. A review of foundational IP theories reveals that specific IP distinguishing traits, such as balance, control and flexibility/evolution, lie at the core of IP assets. If one trait is omitted, altered or distorted by contractual terms the contract may not recognize or support the IP asset. A second cause is conflict between IP distinguishing traits and contract legal principles. These causes both have significant effects for contractual uses of IP assets. Private sector companies may actively draft contracts affected by IP contract strain as influences, such as pressures to attain market advantage, can induce strain. Moreover, emerging IP contract strategies may sustain this activity. Looking through a legal pluralism lens it is evident that causes of IP contract strain may be influenced by legal rules, practice and institutions, collectively or individually. IP contract strain may have numerous far-reaching effects, such as: contracts that do not achieve a contractual use of the whole of an IP asset; defective IP asset chains of title; or systemic problems caused by the number of IP contracts and the reliance placed upon these in the private sector. The outcome of IP co

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)0.013
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0030.006
Scholarly communication0.0090.012
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.043
GPT teacher head0.281
Teacher spread0.238 · 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 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

Citations0
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)→Same topicGender Diversity and Inequality→French-language works237,207→