Representing chemistry culture: ethnography's methodological potential in chemistry education research and practice
Bibliographic record
Abstract
A goal in chemistry education research and teaching is to make chemistry education inclusive to our diverse students. Ethnography is one approach that can support this goal, because it supports researchers and educators in questioning what is considered ordinary by exploring chemistry as a culture. By exploring chemistry as a culture, we can understand how we represent the discipline of chemistry to our students in what we teach, how we teach, and who we teach. Questioning the ordinary aspects of research and teaching can help us work towards creating a more inclusive chemistry culture for our students, researchers, and instructors. Within this perspective, the authors explore ethnography as a research methodology and an approach to understanding experiences in practice. This perspective explores how different choices in research design, such as the research questions, theoretical framework, methods, and methodology framing, lead to different goals and representations of chemistry culture. This perspective aims to start conversations around what we can learn from different representations of chemistry culture for chemistry practice by questioning what is taken for granted in the learning theories chosen, approaches to interventions, and systematic barriers. In its potential to illuminate how chemistry culture is represented and transmitted to students, ethnography can help create more inclusive, accessible, and supportive spaces for learning and interdisciplinary research.
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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 | 0.164 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".