Braiding history, inquiry, and model-based learning: A collection of open-source historical case studies for teaching both geology content and the nature of science
Bibliographic record
Abstract
Many of the current issues facing humans are geologic in nature. Whether the issue is mitigating the impact of geologic hazards, reducing air and water pollution, managing the energy of mineral resources, or minimizing the impact of anthropogenic global climate change, we require a geosciences-literate population. This is crucial to developing policies to best address these issues and to voting to implement such policies. In this article, we introduce a novel strategy for teaching geoscience content, as well as the nature of science, to a diverse audience: the historical case study. Essentially, the historical case studies “braid” the separate strands of history, inquiry, and model-based learning into a narrative, thus allowing students to experience science in the making. This is in contrast to ready-made science as traditionally taught. We discuss the generations of the cases, summarize their content, and describe their implementation in multiple contexts of different courses. We also provide insights from the undergraduate researchers who developed the cases, the instructors who implemented them, and some preliminary data on student knowledge development concerning both geoscience content and nature of science. The cases are available online and open to the public. We welcome any feedback you wish to share.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".