Generating conversations on geoethical education through arts-integrated research
Bibliographic record
Abstract
The applications of geoscience knowledge to complex interdisciplinary socioenvironmental challenges, including climate change and sustainability, are broadly acknowledged. The ways we educate geoscience professionals to make ethical decisions in how geological knowledge is applied to these existential challenges can have profound impacts on the Earth system. Proponents of improving ethical applications of geoscience knowledge and ethical decision-making for geoscientists have identified post-secondary educational settings as essential sites of exposure to geoethical thinking. However, discussions of geoethical pedagogy may not address the ways geoscience knowledge production continues to further exacerbate these problems. Post-secondary geoscience educators, with little ethical or interdisciplinary training themselves, may be ill-equipped to navigate the potentially uncomfortable norm-challenging classroom conversations that can arise. This research applies arts-integrated methodologies to analyze events from my professional roles a geologist and reflections from my current position as a post-secondary geoscience educator, working in a Canadian context. Results of this work offered counter-perspectives to some of the normalized narratives about professional roles of geoscientists, especially: 1) Geoscientists as environmental stewards, 2) Geoscientists as providers of raw materials essential to society, and 3) Geoscientists on a journey of scientific discovery. Questioning these stories proved to be uncomfortable, opening conversations in which both students and educators may feel implicated in ongoing harms. Proposals to improve geoethical education for post-secondary geoscience students may need to better account for the challenging experiences that arise for both students and teachers as difficult knowledge is engaged in the classroom. Here, visual art is used as a tool to bring together perspectives across dissonant knowledge paradigms, and in the process to open up new questions about geoethical education. Though arts-integration is common in educational research, this methodology remains extremely rare for research focused on post-secondary technoscientific training. I contend that this novel application offers the opportunity to open up different conversations about both ethical applications of geoscience knowledge and the challenges of geoethical training.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.044 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.003 | 0.006 |
| 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".