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Record W2015522897 · doi:10.1145/1655048.1655052

On the operational semantics of rights expression languages

2009· article· en· W2015522897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Rights Management and Security
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceProgramming languageCorrectnessExpression (computer science)Operational semanticsCompilerSemantics (computer science)Consistency (knowledge bases)XMLInterpreterArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

We propose an operational model for formalising and enforcing rights expression languages based on the concept of a rights expression compiler. Our compiler transforms an XML-based rights expression into a programme for a virtual machine. This approach provides a formal way of defining semantics for rights expression languages that can be directly used in practice to enforce the expressions while ensuring their consistency and correctness. We further argue that our model eliminates a number of limitations in previous attempts to associate rights expression languages with formal semantics. We demonstrate the power and practicality of our model by using it to develop operational semantics for the OMA Rights Expression Language, from which a real interpreter can be derived with relatively little effort.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.097

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 studies0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Quick stats

Citations8
Published2009
Admission routes1
Has abstractyes

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