Collecting insects to conserve them: a call for ethical caution
Bibliographic record
Abstract
Abstract Insect sampling for the purpose of measuring biodiversity – as well as entomological research more generally – largely assumes that insects lack consciousness. Here, we briefly present some arguments that insects are conscious and encourage entomologists to revisit their ethical codes in light of them. Specifically, we adapt the Three Rs, guidelines proposed in 1959 by WMS Russell and RL Burch that have become the dominant way of thinking about the ethics of using animals in research. The Three Rs specify the need to replace, reduce, and refine the use of animals in research, yet have received little attention in entomological circles, which is perhaps unsurprising given that Russell and Burch explicitly excluded invertebrates from their purview. As a specific case, we consider issues of suffering and bycatch in the use of Malaise traps for insect sampling. While we do not claim that entomologists have an obligation to adopt the Three Rs framework wholesale, we do suggest that there is reason to adopt it in a modified form to mitigate moral risk especially in the context of conservation.
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 | 0.115 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.019 | 0.040 |
| Insufficient payload (model declined to judge) | 0.002 | 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".