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Record W3111675758 · doi:10.23889/ijpds.v5i5.1557

Building Research Capacity and Organizational Empathy Among Students: Making Connections Beyond the Data

2020· article· en· W3111675758 on OpenAlexaffabout
Anita Durksen, Shannon Struck, Alexandra Guemili, Soomin Han, Emily Brownell, Alyson Mahar, Nathan Nickel, Randy Fransoo, Marni Brownell, Jennifer Enns, Lorna Turnbull

Bibliographic record

VenueInternational Journal for Population Data Science · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGeneral partnershipPublic relationsEmpathyGovernment (linguistics)Inclusion (mineral)Qualitative propertyPopulationSociologyKnowledge managementPolitical sciencePsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

IntroductionThe leveraging of multi-sector, whole-population, linked administrative data is advantageous for conducting research on complex real-world problems. However, such large and complex data repositories can sometimes appear impersonal and overwhelming. Establishing organizational empathy (OE) in thecontext of a multi-sector partnership between academic, government and community representatives can help us understand the data better for social policy research. Evidence stemming from this research can then inform policy decisions, ultimately increasing the potency of linked data analysis and creating more meaningful student experiences. Our objective is to examine the role of OE in the student research experience.
 Objectives and ApproachSPECTRUM (Social Policy Evaluation Collaborative Team Research at Universities in Manitoba) is a multi-disciplinary partnership working to provide evidence-based solutions to ‘wicked’ social issues by using linked data from multiple sectors. SPECTRUM provides fellowships to students to become partners in the collaboration. Students have participated in quarterly workshops, building relationships with community leaders, government decision-makers and academic researchers. Students are from various faculties, bringing their unique frameworks and research interests to the collective. Through OE, students observeand participate in SPECTRUM, relating its goals and outcomes to society and their own research.
 ResultsStudent inclusion in SPECTRUM enhances the partnership by providing a greater range of perspectives and facilitates the development of OE among SPECTRUM members. Students are using linked administrative data, while actively engaging in dialogue with stakeholders, thereby enriching their knowledge and understanding of research.
 Conclusion / ImplicationsData linkage involves more than just use of the repository; it requires establishing common ground since the data have different meaning to each partner. OE developed through SPECTRUM provides invaluable insight into and context for the data. Knowledge transfer among members of the partnership will enrich SPECTRUM’s research outcomes while building capacity among Students.

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)0.021
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0130.007
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 head0.584
GPT teacher head0.573
Teacher spread0.011 · 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
Published2020
Admission routes2
Has abstractyes

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