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Record W2114257409 · doi:10.1071/nb08032

Regulation of research through research governance: within and beyond NSW Health

2009· article· en· W2114257409 on OpenAlexaff
Geoffrey S. Bloom, Deborah Frew

Bibliographic record

VenueNew South Wales Public Health Bulletin · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsImpact
Fundersnot available
KeywordsCorporate governancePublic administrationPromotion (chess)Quality (philosophy)BusinessPolitical sciencePublic relationsLawPoliticsFinance

Abstract

fetched live from OpenAlex

Research governance takes a broad approach to the regulation of human research encompassing: (a) frameworks and systems over ad hoc policy making; (b) quality standards as well as regulatory requirements; and (c) definition of roles and responsibilities of all parties involved in research. The effective and consistent implementation of research governance plays a role in the promotion of quality research. NSW Health has recently issued several policies and procedures in relation to research governance. However, for regimes of research governance to achieve optimal effectiveness, they must be consistent with each other in both the public and private sectors and across Australian jurisdictions.

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)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.162
GPT teacher head0.440
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2009
Admission routes1
Has abstractyes

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