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Record W6995551121

One size does not fit all : organizational diversity in New Zealand tertiary sector ethics committees

2016· other· en· W6995551121 on OpenAlexaboutno aff

Bibliographic record

VenueUnitec Research Bank (Unitec Institute of Technology) · 2016
Typeother
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsResearch ethicsAutonomyDiversity (politics)Ethics committeeChristian ministryInformation ethicsConflict of interest
DOInot available

Abstract

fetched live from OpenAlex

Since 1988 in NZ all university and funded health researchers have been mandated to seek ethical review for research projects
\nAt the time, the Ministry of Health ethics committees were guided by an operational standard for health research, yet no equivalent national ethics statement has been produced to guide all University research in NZ (unlike the situation in Canada and Australia)
\nAcademics are justifiably questioning of institutional efforts to temper their autonomy unnecessarily, but little is known – outside of local/individual experiences – about how ethics committees actually work
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\nThis current project seeks to identify strengths of alternative approaches in particular institutional circumstances. It maintains a critical edge centred on improving appropriate access to committee processes and deliberations, and on improving the potential ‘educative’ (vs. governance) focus of ethics committees.
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\nSome key findings: 
\nNo two committees share even broadly similar organizational structures. Four of the five committees are centralised, but the ways in which they operate differ significantly
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\nResearchers have a variable range of access to advice and consultation, and they tend not to use the optional provisions that exist
\nAll five committees are involved in facilitating (varying) learning opportunities within committees and/or in exchanges with others

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.138
GPT teacher head0.380
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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