MétaCan
Menu
Back to cohort

Protection of Intellectual Property Rights in the Development and Utilization of the Dissertation Process

2012· article· en· W1843442515 on OpenAlexvenueno aff
Zhu Yujing

Bibliographic record

VenueCross-cultural communication · 2012
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertySubject (documents)AuthorizationAgency (philosophy)Law and economicsProcess (computing)Key (lock)Development (topology)Property (philosophy)BusinessPolitical scienceLawComputer scienceSociologyComputer securityEpistemologySocial scienceLibrary science

Abstract

fetched live from OpenAlex

This paper explicitly identifies the copyright subject of the dissertation, and focuses on intellectual property rights issues of dissertation; and the author as its copyright owner, in the commercial development and feasible solutions. Key words: Dissertation; Subject of right; University; Paper development agency; Commercial development; Authorization; Royalty

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)0.064
metaresearch head score (Gemma)0.159
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.159
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0080.017
Scholarly communication0.0280.017
Open science0.0020.010
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0170.009

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 head0.068
GPT teacher head0.331
Teacher spread0.263 · 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 designNot applicable
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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCross-cultural communicationSame topicLaw, AI, and Intellectual PropertyFrench-language works237,207