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Record W3123283455 · doi:10.22054/jplr.2020.41847.2208

تحلیل رویه عملی نظام کپی رایت راجع به حق معنوی

2020· article· fa· W3123283455 on OpenAlexaboutno aff
محمد هادی میرشمسی, میلاد حامدی

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languagefa
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Moral right is one of granted rights to author in literary and artistic property law. Unlike economic right which considers protection of financial and economic aspects of author's rights, the function of moral right is to protect author's Character creativity, immaterial aspects and social interests. Despite the attention of countries following copyright system to economic right and having utilitarian approach, necessities such as internal and international (like the results of accession to Berne Convention) requirements, compelled these countries including UK and US to accept moral right. However, this right in these countries has less important situation, more limited instances and time protection rather than countries following author's right system: for example, waiver of moral right in UK has been accepted extensively and the rights such as right of attribution and right of integrity are not applied about some works. Moral rights in Canada are limited into two rights of attribution and integrity and waiver of this right is allowed generally or partially. Australia has the most comprehensive statute on moral right, infringement and remedies about that. This research examines the causes of accepting moral right in copyright system through comparative study on some of countries following this system.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0130.014
Open science0.0080.006
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.3640.004

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 head0.397
GPT teacher head0.515
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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