Don't use it? Don't lose it! Why active use is not required for stimuli, resources or “enrichments” to have welfare value
Bibliographic record
Abstract
Current frameworks for designing and evaluating good enclosures and "enrichments" typically focus on animals' active interactions with these features. This has undoubtedly improved the welfare of zoo-housed animals over the last 30 years or more. However, literature reviews from this same period identify persistent gaps in how such frameworks are applied: experiences and behaviors that do not rely on active interaction with stimuli or resources are largely ignored, when evaluating the welfare value of enclosures and enrichments within them. Here, we review research evidence demonstrating that active interaction is not always a reliable measure of welfare value, showing that items that elicit little or no interaction can nevertheless still reduce stress and improve well-being. This evidence largely comes from research on humans, lab animals and farm animals, but also from some zoo studies too. We then investigate why. We review psychology and ethology literatures to show that such welfare benefits can arise from five, non-mutually exclusive, processes or mechanisms that are well-understood in humans and domestic animals: (1) some motivations are sated quickly by interaction with resources, yet still have large welfare benefits; (2) active interaction may just be a way to achieve a goal or solve a problem, without being beneficial for welfare in itself; (3) having opportunities for choice and control may be inherently beneficial, even when not acted on; (4) some enclosure features meet social needs for structure, landmarks, and blocked sightlines; and (5) some stimuli may be preferred because they signaled good environments to an animal's ancestors. We use this information to identify improved ways of enhancing and assessing zoo animal welfare. Incorporating these concepts should expand the scope of behaviors and subjective experiences that are targeted, to now include those that involve little active interaction and yet still are important for good welfare.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".