The 2024 ASPIRE Research Program Symposium Abstract Booklet
Bibliographic record
Abstract
The ASPIRE (Advocacy, Support, Perseverance, Innovation, Research, Experience) Research Program, founded by Shania Sheth and Siddharth Seth, provides students with early research experience and the opportunity to conduct their own research project. In teams of 2-5, our cohort of 30 undergraduate students worked together over a span of 4 months to create and execute a novel research project on a topic of their choice. Students received guidance by experienced student research mentors throughout the process, allowing students to develop and refine essential skills. This year, the program focused on teaching students about how to develop effective search strategies, how to perform data extraction, and how to thematically analyze research papers to construct a well-structured narrative review. Within their teams, students presented results of their narrative literature reviews in the form of research posters at the ASPIRE Research Symposium, a full-day event filled with guest speakers and networking opportunities. Presented in this abstract booklet is the culmination of months of hard work, dedication, and achievement. We hope you enjoy reading through the 2024 ASPIRE Research Program abstracts. We are very excited to see what our aspiring researchers achieve next! For future opportunities, please reach out to aspire.research.program@gmail.com.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.564 | 0.370 |
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".