Richer Connections to Robotics through Project Personalization
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
In this work, we describe youth outreach activities carried out under the Chair for Women in Science and Engineering for Ontario (CWSE-ON) program. Specifi cally, we outline our design and implementation of robotics workshops to introduce and engage middle and secondary school students in engineering and computer science. Toward the goal of increasing the participation of women in science and engineering, our workshop design incorporates strategies presented in work by Rusk et al. (2008) on broadening participation in robotics: 1. focusing on themes, not just challenges; 2. combining art and engineering; 3. encouraging story-telling; and 4. organizing exhibitions, rather than competitions (Rusk et al., 2008, page 1) We discuss three workshop themes designed to highlight creativity and provide choices to participants. Our “Wild in the Rainforest” workshops make use of the PicoCrickets robotics kits and software used and described by Rusk et al. (2008). We also present Lego Mindstorms workshops themed “So You Think Your Robot Can Dance” and “A Day at the Park”. Our workshops are presented by female role models with academic backgrounds in science and engineering. Although workshop periods are fairly short (60-90 minutes), participants learn that robots have perception, cognition, and action – and are tasked with designing and programming to highlight these abilities. We present the results of our workshops through images and videos of the teams’ creations. Workshop evaluation data provided by participants demonstrate that our approach results in rich connections to engineering and technology for participants of both genders.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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