Proceedings from the 2022 Indicium Conference
Bibliographic record
Abstract
Indicium is an annual research mentorship program and competition geared towards introducing interested students to the process of independent research. Through this mentorship program, undergraduate students are grouped with mentors or principal investigators (e.g., PhDs, professors, graduate students, medical students) who actively guide and support student teams to conduct research. With the mentors’ guidance, Indicium participants are fully prepared for the final Indicium Research Conference. The program also includes workshops focusing on various aspects of project development as well as networking opportunities within the greater scientific community. This year, Indicium expanded across five universities across Canada and was carried out by the STEM Fellowship branches at McMaster University, University of British Columbia, York University, University of Toronto St. George, and University of Toronto Mississauga. Every branch held a university-level conference where participating teams submitted to have their abstracts to be published in the proceedings below. Eight teams selected from these branches moved forward to participate in the National Indicium research Conference held on July 9, 2022. Winning research projects from this final competition are invited to submit their work to the STEM Fellowship Journal, pending that it passes peer review. We are pleased to be showcasing the conference proceedings from the five participating Canadian universities. It was our pleasure working alongside an incredible group of mentors, mentees, judging panel, and executive team on this initiative. Indicium would not be possible without the drive, engagement, and active participation of all those who were involved to help advance knowledge, mentorship, and future opportunity.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.229 | 0.154 |
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".