2022-2023 Multidisciplinary Health Research Experience (MHRE) Research Pitch Competition
Bibliographic record
Abstract
The Multidisciplinary Health Research Experience (MHRE) Research Pitch Competition 2022-2023 was an inaugural competition hosted at the University of Manitoba, aimed to engage undergraduate students in research thinking and innovation. In this competition, students were offered the opportunity to form teams of 1-3 people, with a graduate student acting as a research mentor. As a team, members collaborated on designing a research proposal, presented as an abstract and poster, to address an issue of their choice that was related to the competition theme. Moreover, the competition theme for this year centred on ‘limiting the spread of infectious diseases during a humanitarian crisis’. Students were encouraged to explore different fields of STEM that interested them for their research proposal. The competition received over 30 team applications spanning various disciplines such as STEM, statistics, biology, microbiology, epidemiology, public health, community health, medicine, social sciences, engineering, and more. The following abstracts feature the top submissions evaluated by both graduate students and professors.
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 | 0.018 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.268 | 0.111 |
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".