Moving On from the Insect Apocalypse Narrative: Engaging with Evidence-Based Insect Conservation
Bibliographic record
Abstract
Abstract Recent studies showing temporal changes in local and regional insect populations received exaggerated global media coverage. Confusing and inaccurate science communication on this important issue could have counterproductive effects on public support for insect conservation. The insect apocalypse narrative is fuelled by a limited number of studies that are restricted geographically (predominantly the United Kingdom, Europe, the United States) and taxonomically (predominantly some bees, macrolepidoptera, and ground beetles). Biases in sampling and analytical methods (e.g., categorical versus continuous time series, different diversity metrics) limit the relevance of these studies as evidence of generalized global insect decline. Rather, the value of this research lies in highlighting important areas for priority investment. We summarize research, communication, and policy priorities for evidence-based insect conservation, including key areas of knowledge to increase understanding of insect population dynamics. Importantly, we advocate for a balanced perspective in science communication to better serve both public and scientific interests.
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 | 0.182 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".