Entomology in the 21st Century: Tackling Insect Invasions, Promoting Advancements in Technology, and Using Effective Science Communication—2018 Student Debates
Bibliographic record
Abstract
The 2018 student debates of the Entomological Society of America were held at the Joint Annual Meeting for the Entomological Societies of America, Canada, and British Columbia in Vancouver, BC. Three unbiased introductory speakers and six debate teams discussed and debated topics under the theme 'Entomology in the 21st Century: Tackling Insect Invasions, Promoting Advancements in Technology, and Using Effective Science Communication'. This year's debate topics included: 1) What is the most harmful invasive insect species in the world? 2) How can scientists diffuse the stigma or scare factor surrounding issues that become controversial such as genetically modified organisms, agricultural biotechnological developments, or pesticide chemicals? 3) What new/emerging technologies have the potential to revolutionize entomology (other than Clustered Regularly Interspaced Short Palindromic Repeats)? Introductory speakers and debate teams spent approximately 9 mo preparing their statements and arguments and had the opportunity to share this at the Joint Annual Meeting with an engaged audience.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 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".