Fresh Ideas: Side by Side Summer Research Program Conference 2021
Bibliographic record
Abstract
Side by Side Agency (SBS) is a student-led educational non-profit based in the Greater Toronto Area. We began by providing free virtual tutoring and mentorship for K-12 students to compensate for pandemic-related educational disruptions. When school services returned, we switched gears to focus on persisting barriers to academia for marginalized students primarily addressing inconsistencies in research education across Ontario. Literature on Canadian undergraduate students’ literacy and research skills affirmed our personal difficulties with entering the research field, motivating us to develop a free, accessible, and supportive beginner research program for high school students. The 2021 SBS Summer Research Program (SBS SRP) was developed by undergraduate volunteers and included weekly lessons, workshops, homework, and guest speakers to cultivate research skills and inspire curiosity. Students worked with undergraduate mentors to pursue research projects of their interest in fields such as neuroscience, public health, and sociology. SBS SRP culminated with our conference, where students presented their literature reviews, proposed studies, and informative social media campaigns. Check out our website to view our students’ presentations and learn more.
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.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.216 | 0.098 |
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".