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Sharing the cost: health information licensing programmes in Canada

2009· article· en· W2055312343 on OpenAlexaffabout
Vivian Stieda, Marijana Bačić

Bibliographic record

VenueHealth Information & Libraries Journal · 2009
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsAlberta Health
FundersU.S. National Library of Medicine
KeywordsBusinessResource (disambiguation)Public relationsCorporate governanceDescriptive researchFunction (biology)Shared resourceDescriptive statisticsKnowledge managementMedicinePolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Increasing pressure on health libraries to provide access to electronic resources to their clientèle has witnessed the establishment of a number of initiatives in Canada's provinces. OBJECTIVES: To provide a structured and descriptive account of Canadian initiatives that focus primarily on licensing health-related information to post-secondary education institutions, hospitals, libraries and related organisations in the health sector. METHODS: Programmes were identified via communication with peers, an unpublished paper and the authors' existing knowledge. This resulted in a survey of the programmes using an online questionnaire. RESULTS: A total of seven programmes were identified. A descriptive account of their establishment, aim and function, governance, funding, resource selection and licensing is provided. CONCLUSIONS: Sharing out the cost of subscribing to electronic resources in the health sciences is a continued concern, as witnessed by continuing developments in this area.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly 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 score0.994
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0070.003
Scholarly communication0.0060.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.234
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2009
Admission routes2
Has abstractyes

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