Proceedings from the 2023 indicium conference
Bibliographic record
Abstract
Indicium is an annual research competition geared towards introducing inexperienced students to the process of independent research. Through this mentorship program, undergraduate students are grouped with PhDs, professors, graduate students, medical students, etc. who are experienced in STEM research fields. Mentors actively guide and support students through a research project in their field of expertise and prepare them 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 was hosted at four university branches across Canada including McMaster University, York University, University of Toronto St. George, and University of Toronto Mississauga. Every branch held a university-level conference where participating teams were granted abstract publications in the National peer-reviewed STEM Fellowship Journal. Ten teams selected from these branches moved forward to participate in the National Indicium Research Conference held on August 12, 2023. Winning teams from this competition have been granted a full manuscript publication. We are pleased to be showcasing the conference proceedings from the four participating Canadian Indicium branches. Working alongside the incredible group of mentors, mentees, judging panel, and executive team on this initiative was an honour. Indicium would not be possible without the drive of those who chose to indulge in this platform in the pursuit of knowledge, mentorship, and future opportunities.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.061 |
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".