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Record W2037414519 · doi:10.1177/1081180x08089318

On the importance of intellectual property rights for e-science and the integrated health record

2008· article· en· W2037414519 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

VenueHealth Informatics Journal · 2008
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsYork University
FundersEconomic and Social Research Council
KeywordsIntellectual propertyData sharingCorporate governanceBusinessKnowledge managementScale (ratio)Medical researchPublic relationsPolitical scienceData scienceComputer scienceMedicineLawAlternative medicine

Abstract

fetched live from OpenAlex

An integrated health record (IHR) that enables clinical data to be shared at a national level has profound implications for medical research. Data that have been useful primarily within a single clinic will instead be free to move rapidly around a national network infrastructure. This raises challenges for technologists, clinical practice, and for the governance of these data. This article considers one specific issue that is currently poorly understood: how intellectual property (IP) relates to the sharing of medical data for research on large-scale electronic networks. Based on an understanding of current practices, this article presents recommendations for the governance of IP in an integrated health record.

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)0.018
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.007
Open science0.0020.000
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 head0.146
GPT teacher head0.373
Teacher spread0.227 · 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