Building Research Capacity and Organizational Empathy Among Students: Making Connections Beyond the Data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.042 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".