MétaCan
Menu
Back to cohort
Record W2916930625 · doi:10.1111/icad.12344

Collecting insects to conserve them: a call for ethical caution

2019· article· en· W2916930625 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInsect Conservation and Diversity · 2019
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSentienceContext (archaeology)ConsciousnessEcologyBiodiversityObligationEnvironmental ethicsEpistemologyBiologyEnvironmental resource managementLawPolitical scienceEconomicsPhilosophy

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.144
GPT teacher head0.328
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it