Using a Social Robot for Student Outreach in an Engineering and Computer Science Library.pdf
Bibliographic record
Abstract
In 2021, the Engineering & Computer Science Library at the University of Toronto purchased a MiRo-E robot (from Consequential Robotics) as part of a grant. The robot was the centerpiece of a hybrid online and in-person programming contest we held as part of our student outreach. Students of the Faculty of Applied Science & Engineering and Department of Computer Science developed code to create interactive behaviour for the robot using the online simulation software MiRoCLOUD, and their code was judged by a panel of librarians, staff, and faculty. Given the cute, zoomorphic, interactive nature of the MiRo-E robot, we also engaged students and staff with a Name that Robot Contest and held meet and greets that students described as being like therapy dog sessions run by other libraries. In this presentation, we will discuss our library’s experience with using MiRo-E as a tool for outreach to STEM students. This will include an overview of how the MiRo-E robot works, the software and hardware involved in its operation, and some possible applications of the robot in an academic library setting. We will also discuss our experience with running the programming contest as a hybrid event, as well as how we approached facilitation and judging of this event with a team who has varying degrees of coding knowledge. Finally, we will discuss the challenges we faced with using MiRo-E as an outreach tool.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.141 | 0.047 |
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".