Semantics of the insect decline narrative: recommendations for communicating insect conservation to peer and public audiences
Bibliographic record
Abstract
Abstract Ambiguous or misleading language can affect science communication with peer and public audiences, with potentially damaging impacts on policy and public engagement. The word decline can be value‐laden and has inherent negative connotations. It is not always the most appropriate term to use for effective science communication to promote insect biodiversity and conservation issues. We recommend four key questions to consider when deciding whether the term insect decline is appropriate to use. Evidence‐based insect conservation depends on public and political support. Moving forward, researchers and communicators should be mindful that the inherent diversity of insects demands a nuanced and diverse scientific discussion, not an ambiguous and generalised one.
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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 | 0.135 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.031 | 0.061 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".