SPARCS from the University of Victoria: Supporting Sustainable and Integrated Outreach Activities for Educators and Young Minds
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
Although IT industry is flourishing, student enrolment in Computer Science and related Engineering post-secondary degree programs is low. The causes and issues surrounding this trend are diverse, inter-related, and vastly complex; a consensus may never be reached regarding the nature of these issues affected by both global and local factors. Is a consensus regarding underlying causes and best practices required to create and sustain an infrastructure for effective outreach? Perhaps not. In this article, we discuss five key supportive mechanisms that are critical to sustained and integrated outreach initiatives at the grassroots level. We discuss these mechanisms using the example of SPARCS (Solving Problems with Algorithms, Robots, and Computers), an initiative at the University of Victoria, British Columbia (BC), Canada. We further consider the tradeoffs associated with a vertical/centralized infrastructure versus a horizontal/distributed infrastructure.
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 | 0.000 |
| 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