Bibliographic record
Abstract
Abstract One of Tinbergen's most lasting contributions to the study of behaviour was the distinction he drew between causal, functional, developmental, and evolutionary questions about behaviour. More recently, behavioural ecologists have claimed that understanding the function of behaviour is an important step towards understanding its causes. This claim has, in turn, been criticised for confusing the fundamental distinction that Tinbergen defined. The study of behaviour, however, usually begins by identifying units of behaviour functionally and only then proceeds to causal analysis. Research carried out on four phenomena — disassortative mating by MHC loci, memory for cache sites in food-storing birds, auditory localisation of prey by barn owls, and magnetic orientation — illustrates the contributions made to causal research through understanding the function of behaviour. Understanding function, and sometimes simply a hypothesis about function, defines the causal questions that are asked, identifies novel questions for causal investigation, and sets the criteria that causal explanations must satisfy.
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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.008 | 0.033 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".