Bibliographic record
Abstract
Abstract Whether insects (Insecta) have the subjective experience of pain is difficult to answer. Recent work in humans demonstrated that the experience of pain occurs due to the activation of a “pain network” that integrates nociceptive sensory information, memory, emotion, cognition, and self-awareness. In humans, the processing of nociceptive sensory information alone does not produce the subjective experience of pain. Insect nociception is processed largely in parallel in two higher-order areas in the brain: the mushroom bodies and the central complex. There is little evidence of a coordinated pain network that would integrate these two areas with each other along with other traits thought to be important for a pain experience in humans. However, it is difficult to exclude the possibility that insects could have a modest pain experience using a less integrated neural circuit. This possibility seems unlikely, however, because even a modest experience would require some neuronal investment. It is unclear whether insects would benefit from such an investment. Recent work in artificial intelligence suggests that relatively simple, cost-efficient circuits can produce adaptive behaviours without subjective experience. Given our current understanding of insect behaviour, neurobiology, and evolution, the likelihood that insects experience pain is low.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".