Adventures in (geo)science communication: mapping outreach practices into university classrooms
Bibliographic record
Abstract
Science, technology, engineering, and mathematics (STEM) subjects have historically struggled to be inclusive and accessible to students from diverse backgrounds. Furthermore, STEM subjects have often been rigid in their teaching structure, creating barriers to education for students with more specific (or unrecognised) learning needs. Our STEM outreach course, Think Like A Scientist, has been running in a number of English prisons since 2019, and started in Canada and Australia over the past two years. Our students in prison often have diverse learning needs – a classroom often presents numerous barriers (sensory, communication, information processing, and regulation) which particularly impacts neurodivergent students (e.g., autism, ADHD, OCD, dyslexia, etc.). In our teaching in prison, we have been conscious of creating different educational access points that are not solely reliant on rigid teaching structures.Although our outreach programme is tailored to the restrictive prison environment, the application of its core principles are fundamental Equity, Diversity, and Inclusion (EDI) practices that can be applied to university-level teaching and supervision. Here, we outline the choices we have made in prison education to increase educational engagement for those within the neurodivergent umbrella – and how these choices can map onto university teaching to widen participation for STEM students. Specifically, we will describe our university campus work in a few key areas: creating relatable science content for our geoscience student body, giving students a voice in their education, adding reflection activities, and fostering a classroom environment that is inclusive and accessible to all. Finally, we welcome an open discussion on potential best inclusive practices in the geosciences.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".