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Record W4417518262 · doi:10.21275/sr251218134248

Orphan Works in Copyright Law: Causes, Challenges, and Comparative Legal Responses

2025· article· W4417518262 on OpenAlexaboutno aff
Charisma Mariam Appachankutty

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsPublic domainIntellectual propertyPublic interestFair useCopyright lawIdentification (biology)MetadataPublic policy

Abstract

fetched live from OpenAlex

This article examines the legal, structural, and technological factors that contribute to the growing problem of orphan works within contemporary copyright systems. It explains how the removal of registration formalities, the steady extension of copyright duration, and weak metadata practices have made ownership identification increasingly difficult, particularly in the digital environment. Using a doctrinal and descriptive research methodology, the paper draws on domestic legislation, international treaties, judicial decisions, and policy reports to assess the scale of the problem and its practical consequences for authors, users, libraries, and cultural institutions. Comparative analysis of approaches in the United States, the European Union, Canada, and India highlight divergent regulatory models ranging from fair use doctrines to licensing and centralized orphan works registries. The discussion shows how legal uncertainty discourages preservation, digitization, and lawful reuse, resulting in a broader loss to public access and cultural memory. The article concludes by outlining policy options and preventive measures, emphasizing the role of diligent search standards, limited liability frameworks, and improved rights information systems in balancing copyright protection with public interest objectives.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.005
Scholarly communication0.0030.006
Open science0.0010.001
Research integrity0.0000.001
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 head0.163
GPT teacher head0.402
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 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
Published2025
Admission routes1
Has abstractyes

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