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Record W2093411079 · doi:10.1177/1098214010379038

Evaluating the Science of Discovery in Complex Health Systems

2010· article· en· W2093411079 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

VenueAmerican Journal of Evaluation · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of British ColumbiaNutrasourceUniversity of Toronto
Fundersnot available
KeywordsProcess (computing)DisciplinePlan (archaeology)Data scienceWork (physics)Engineering ethicsComputer scienceScientific discoveryManagement scienceLogic modelTranslational scienceHealth scienceSociologyPsychologyEngineeringMedicineSocial scienceMedical education

Abstract

fetched live from OpenAlex

Complex health problems such as chronic disease or pandemics require knowledge that transcends disciplinary boundaries to generate solutions. Such transdisciplinary discovery requires researchers to work and collaborate across boundaries, combining elements of basic and applied science. At the same time, calls for more interdisciplinary health science acknowledge that there are few metrics to evaluate the products associated with these new ways of working. The Research on Academic Research (RoAR) initiative was established to evaluate the process of discovery and impact of collaboration that emerged through the Life Sciences Institute (LSI) at the University of British Columbia, a state-of-the-art facility designed to support researchers—self-organized around specific health problems rather than disciplines. A logic model depicting the factors influencing such collaboration is presented along with a multimethod evaluation plan to assist understanding of the discovery process in this new environment and develop new metrics for assessing collaborative impact.

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)0.064
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0640.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.289
GPT teacher head0.584
Teacher spread0.295 · 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