Global disparity in public awareness of the biological control potential of invertebrates
Bibliographic record
Abstract
Invertebrates make up 97-99% of biodiversity on Earth and contribute to multiple ecosystem services (ES) in both natural and human-dominated systems. One such service, biological control (BC) of herbivorous pests, is a core component of sustainable intensification of agriculture, yet its importance is routinely overlooked. Here we report a macro-scale, cross-cultural assessment of the public visibility (or ‘awareness’) of BC invertebrates, using high-throughput analysis of large bodies of digitized text. Using binomial scientific name frequency as proxy for awareness, we compared the extent to which a given species featured in webpages within either scientific media or the entire worldwide web, and in total search volume at varying spatial scale. For a set of 339 BC invertebrate species, scientific and internet coverage averaged 1,020 and 1,735 webpages, respectively. Substantial variability was recorded among BC taxa with Coleoptera, Hemiptera and Nematoda having comparatively high visibility. Online visibility exhibited large geographical variability ranging from France covering BC invertebrates on average in 1,050 webpages versus USA on just 31. This work represents the first extensive use of culturomics to assess public visibility of insect-mediated ES. As BC uptake is dictated by stakeholders’ access to (agro-ecological) information, our work identifies geographically-delineated areas that are differentially attuned to the concept of invertebrate BC, pinpoints opportunities for focusing education campaigns and awareness-raising, enables real-time tracking of BC public appeal, and informs public policy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".