Recent history and future trends in the study of insect behavior in North America
Bibliographic record
Abstract
Abstract We examine the recent history and future trends in the field of insect behavior in North America. This project stemmed from participation in a section symposium at a Joint Meeting of the Entomological Societies of America (ESA), Canada (ESC), and British Columbia (ESBC) in Vancouver, British Columbia, Canada, in 2022. Each participating team in the symposium was asked to address 3 questions about their subdiscipline: (i) How has your subdiscipline changed in the last 15 yr? (ii) How will your subdiscipline change as a discipline over the next 15 yr? (iii) What can ESA and the Plant-Insect Ecosystem section (P-IE) do to help members who study your subdiscipline and improve the understanding of the subdiscipline? To address the first question, we used data from 2008 to 2022 on presentations given at ESA meetings, literature searches, and funding databases. To predict changes in the discipline of insect behavior in the future, we examined data pertaining to student participation in the field and educational opportunities in insect behavior. Our main findings are that insect behavior is an integral part of entomological research with an important future role to play in understanding insect biology under climate change. We provide multiple lines of evidence illustrating the importance of insect behavior in multidisciplinary research across a variety of scientific fields. We conclude by answering the third question with suggestions for the promotion of insect behavior research at annual ESA meetings and for gathering more information to further understand the importance of the subdiscipline of insect behavior.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".