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Record W3135332888 · doi:10.24191/ajue.v16i4.11960

Liberating Orphan Works from The Copyright Orphanage: The Malaysian Perspective

2021· article· en· W3135332888 on OpenAlexaboutno aff
Muhamad Helmi Muhamad Khair, Haswira Nor Mohamad Hashim

Bibliographic record

VenueAsian Journal of University Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsnot available
FundersUniversiti Teknologi MARA
KeywordsLegislatureOrphan drugPhenomenonWork (physics)ParliamentPerspective (graphical)Subject (documents)Political scienceLaw and economicsPublic relationsLawEngineeringEconomicsComputer sciencePolitics

Abstract

fetched live from OpenAlex

Orphan works are works that are still protected by copyright and whose owners cannot be identified or located by prospective users for copyright clearance. Many countries have addressed this issue since the emergence of the problem, and it remains a legitimate subject of inquiry in this present day. However, Malaysia is yet to initiate public consultations and formulate legislative and non-legislative solutions to the orphan work problem. Hence, this paper aspires to underline the challenges and obstacles in exploiting the orphan works in Malaysia. It starts with a brief introduction to the orphan works problem and its causes. It further highlights the legal and policy uncertainties about the orphan work phenomenon in Malaysia and its implication to higher learning education. Besides, this paper also examines the current practices in the United Kingdom and Canada. Finally, this paper proposes some suggestions into what Parliament and policymakers have to do and avoid when solving Malaysia's orphan work phenomenon. It is hoped that the access to the orphan works in Malaysia would not be problematised, thereby liberating them from the copyright orphanage. 
 
 Keywords: Copyright law, Orphan works, Orphan works licensing scheme

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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