Building confidence in STEM students through breaking (unseen) barriers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Science, technology, engineering, and math (STEM) subjects have historically struggled to be inclusive and accessible to students from diverse backgrounds. The field of geoscience, in particular, has also had challenges in diversity with respect to staff and student recruitment. The consequence of non-inclusive practices still propagates today, with certain demographics not engaging in STEM activities. As a result, there needs to be conscious efforts to adopt equity, diversity, and inclusive (EDI) initiatives for subjects such as geoscience to grow. In this article, we outline the steps we have taken to break down known (and unknown) barriers to education in the teaching of a science outreach course to a diverse student body. Our outreach course, Think Like A Scientist, has been running in a number of English prisons since 2019. Although the programme is tailored to the restrictive prison environment, the application of its core principles to education are fundamental EDI practices that could be beneficial to a wide audience. In this paper, we outline our reasoning for specific pedagogical choices in the classroom when working with students that have low confidence in STEM education, and we highlight the need for engagement that is not only relatable, accessible, and inclusive but also offers encouragement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it