The Role of the Faculty Advisor for a Model United Nations Conference: 5 Lessons Learned from Practice
Bibliographic record
Abstract
A Model United Nations (MUN) conference is one of Political Science’s most enduring and iconic formats for active learning and a defining event for many undergraduate students and high school students. Despite its established place in the discipline of Political Science, a MUN conference is an event that defies attempts at perfection, due mainly to the experience being out of the hands of faculty. This paper identifies five lessons learned from delivering hybrid MUN conferences at a mid-sized university campus (6,500 undergraduate students) between 2015-2019. The faculty member collaborated with upper-level undergraduate student organizers to organize and deliver conferences that included delegates participating in partial fulfillment of course credit, delegates from local high-schools, and university students participating for the experience. The connecting theme of these lessons is a consideration of achieving balance between the two roles of the participating faculty member as both the authoritative decision-maker, and that of a delegating supervisor providing oversight and allowing students the freedom to assume the responsibilities of serving as secretariat or as delegate.
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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 | 0.049 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".