How to wreck your own presentation: Twelve tips to confuse an audience
Bibliographic record
Abstract
Increasingly, professional foresters are expected to participate in communication and consultation processes to present specialised knowledge to non-foresters. However, forest management is increasingly complex; forestry is too important to be left in the hands of those who do not understand its intricacies. In this tongue-in-cheek paper, we provide professional foresters with twelve easy-to-use tips to ensure that presentations to non-foresters remain hermetic, confuse the public and preserve the exclusivity of our professional competence. Forging an unclear message, finding a bad title, and failing to adjust to listeners are just the first steps to success in boring an audience. Over-confidence in technical gadgets and an over-powering use of presentation backgrounds, fonts, and special effects will add to the confusion. Furthermore, efforts should be made to conceal the key message by hiding the big picture, maximizing the quantity of information, and using slides that no one will remember. Jargon is highly effective and should be used wherever possible. The speaker should treat the audience as an amorphous crowd, avoiding contact with individuals and dodging questions. We finally suggest using the last slide as an ultimate weapon to ensure that everyone leaves the room more confused than when they arrived. We hope that these simple tips will help professional foresters across Canada to make the most of opportunities for presentations, thereby reinforcing the correct role of the public in forestry. Key words: presentation, communication, public consultation
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.040 |
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".