Two paradigms of research and their influence on the study of animal behaviour
Bibliographic record
Abstract
Taxonomist Roy Crowson identified a fundamental difference among sciences. What he called “natural history” is exemplified by classical biology; it begins by observing, describing and classifying phenomena in the real world, then seeks patterns and concepts that help to synthesize and explain the observations, and proceeds to measurement and experimentation grounded in that framework. What he called “natural philosophy” is exemplified by classical physics; it postulates fundamental principles or concepts that are thought to apply universally, and the research proceeds more directly to measurement and experimentation directed at these principles or concepts. The study of animal behaviour has been influenced by both paradigms. The early ethologists (and now many zoologists) observed, described and classified the natural behaviour of various species and developed explanatory models based on the observations. Behavioural psychologists, in contrast, tended to focus on learning and motivation, sought laws or postulated constructs that were thought to apply universally, and then used simple measurements of artificial actions, usually with laboratory rodents, to develop laws or test theories. Both paradigms have been applied to the study of social behaviour and affective states, and have led to very different methods. Following the natural history paradigm, scientists have observed, described and classified how individuals interact with each other, how free-living groups of animals are organized, and have looked for evidence of affective states underlying the behaviour; they then developed concepts (facultative siblicide, matriarchy, separation distress) and tested hypotheses that helped make sense of the observations. Following the natural philosophy paradigm, other scientists postulated general concepts or constructs (aggression, dominance, emotionality) that were presumed to apply universally, and then used simple, often contrived, measurements to better understand them. The influence of the paradigms can be seen in applied studies of animal welfare and in the use of “animal models” of human mental and emotional conditions. I argue that scientists need to decide critically which paradigm to follow at a given point in their research, paying particular attention to (1) whether the measurement methods are valid, especially when they were not designed based on the natural behaviour of the species, (2) whether the concepts invoked are the most useful for the issue at hand, and (3) whether and when the concepts and findings can truly be generalized across species. • The study of animal behaviour has been influenced by two main paradigms. • One grounds concepts and theories in description and classification of behaviour. • The other proceeds more directly to measurement of existing high-level constructs. • The paradigms profoundly influence research methods and topics. • They also influence the validity, usefulness and generalizability of the research.
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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 | 0.051 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.104 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".