Bibliographic record
Abstract
This research paper examines a student-partnership project on developing the first undergraduate student conference in the Social Psychology Program at McMaster University. This article first situates our research within the broader pedagogical literature on student partnership and engagement in research-based learning opportunities, followed by an overview of the research methodology, including a discussion of the exit survey administered to the first conference participants in 2022. Further, we discuss significant findings and challenges that arose from this student-partnership project by reflecting on the experiences of the student-partner team members, examining feedback received from undergraduate student conference participants at the first undergraduate conference in the Social Psychology Program, and discussing how the original conference in January 2022 informed the development of the March 2023 and March 2024 conferences. We conclude with suggestions for future research in this area of student-partnership based research and share the lessons learned.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.027 | 0.043 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".