Giving Better Presentations: Small Tweaks for Big Impact
Bibliographic record
Abstract
The purpose of my presentation is to provide small suggestions to students from all disciplines to improve their next oral presentation. Depending on a student's faculty of study, they may never have to make an oral presentation. Throughout my career in sales, marketing and broadcasting, I have picked up small tricks around voice, non-verbal communication, room use and presentation aids. This short presentation aims to give students in faculties where presentation skills are not taught some tips to improve their next presentation. My desire is that students will take away at least one suggestions to try during their next presentation. In addition to my own lived experiences, my presentation utilizes secondary research to back-up my experience and bolster my key take-aways. My expertise has afforded me the opportunity to speak at conferences (3M Marketing Excellence Awards), compete in international business case competitions (John Molson Case Competition, Richard Peddie Case Competition), perform in radio broadcasting (with Central Ontario Broadcasting and Bell Media Radio) and has helped me achieve awards in the field (3M Marketing Excellence Award and Canadian Podcasting Awards, for example). Keywords: Microsoft Powerpoint, Presentation Skills, Verbal and Non-Verbal Presentations
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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 | 0.015 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.397 | 0.242 |
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".