MétaCan
Menu
Back to cohort
Record W2889745882 · doi:10.23889/ijpds.v3i4.671

SAGE: supporting secondary data analysis and expediting knowledge mobilization with linked administrative, service delivery, and research data

2018· article· en· W2889745882 on OpenAlexaboutno aff
Hannah Lloyd-Jones, Robert Jagodziński, Poliana Gonçalves Barbosa, Jason Lau, Xinjie Cui

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsData sharingData governanceTransparency (behavior)Data securityKnowledge managementPublic relationsBusinessService (business)Computer scienceData qualityComputer securityPolitical scienceMarketing

Abstract

fetched live from OpenAlex

IntroductionThe cultural revolution of data sharing is becoming a global movement. It allows for scientific replication and verification of research results, avoiding research duplication, and enabling greater transparency and knowledge mobilization with a relatively low cost. However, privacy protection and data security are critical concerns for human-subject related data sharing.
 Objectives and ApproachIn order to facilitate data sharing and engage various stakeholders to better inform policy and practice while protecting privacy, SAGE (Secondary Analysis to Generate Evidence) was established by PolicyWise for Children and Families. It is a collaborative data repository platform that connects stakeholders through secondary use of data. SAGE was built to link, manage, and share research data, community service data, and administrative data related to health and social well-being. Governance and technical processes are in place to ensure that data depositors are involved in decision-making, and data accessed by collaborators are secured and re-identification risks assessed.
 ResultsSAGE has been in operation for over a year. Through engagement with the research and non-profit communities, SAGE now offers ten data assets. Discovery is facilitated through well-documented metadata through NADA and Dataverse. Six new collaborative projects have been initiated through SAGE. SAGE is working actively with local non-profits to liberate data to generate evidence and collaborate with each other on common goals. SAGE has helped these organizations understand the legal and legislative barriers to data sharing, and build the technical capacity to further this goal. Discussions are underway with Alberta public entities on how SAGE can support the linkage and governance processes in the use of administrative data.
 Conclusion/ImplicationsSAGE is putting the governance processes and security practices in place to fill a need for a facilitated data sharing model for sensitive data. SAGE is supporting the cultural shift towards data sharing and reuse by fostering trust and collaboration among researchers, non-profit and government ministries.

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)0.022
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0030.005
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 head0.727
GPT teacher head0.680
Teacher spread0.047 · 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.

Study designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal for Population Data ScienceSame topicEthics in Clinical ResearchFrench-language works237,207