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Record W2021150228 · doi:10.12927/hcpol.2014.23732

Strategically Investing in Health Research: The Little-Big Program Called RPP

2014· article· en· W2021150228 on OpenAlexaffvenueabout
Penny Moody-Corbett

Bibliographic record

VenueHealthcare policy · 2014
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNova scotiaFellPolitical scienceInvestment (military)Research programEconomic growthGeographyEconomicsArchaeologyPolitics

Abstract

fetched live from OpenAlex

The Regional Partnerships Program (RPP) was a program of the Canadian Institutes of Health Research (CIHR) and six provinces - Saskatchewan, Manitoba, New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland and Labrador. CIHR and each province contributed 50% to support health research that was recommended for funding by peer review but fell below the CIHR budgetary cut-off for funding. The provinces would like to commend CIHR for this strategic initiative and highlight the impact that small investment like RPP has had on engaging these provinces in the health research enterprise and expanding health research and its benefits across the country.

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)0.063
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.007
Scholarly communication0.0110.005
Open science0.0040.026
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0090.002

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 head0.705
GPT teacher head0.610
Teacher spread0.096 · 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
DomainIncentives
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
Published2014
Admission routes3
Has abstractyes

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