Data Science Pedagogy to Support Industry, Governmental, and Research Initiatives
Bibliographic record
Abstract
Data Science practices are increasingly leveraged in disparate domains of research, whether as part of industry workflows, governmental department initiatives, or open problems within academic communities. Herein, we describe designing term-projects to introduce senior undergraduate students to applied Data Science research for industry, governmental, or academic "clients" through a series of course assignments and client meetings. We outline the lessons learned and describe how they may be adapted within similar courses. Students are familiarized with data science best practices, obtain applied research experience, and (potentially) professionally benefit from an actual research contribution in the form of a peer-reviewed conference publication; at time of writing, we have published three student-led projects in the proceedings of eminent peer-reviewed conferences. We highly recommend introducing undergraduate students to such client-serving research applications early in their program to encourage them to consider pursuing a research-focused career path.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".