Using Rare Breeds in Animal-Assisted Activities: A New Model Proposed at the “Animal Farm” in Ladispoli (Rome, Italy)
Bibliographic record
Abstract
In Europe many indigenous local breeds of domestic animals are at risk of extinction; the European Union supports multi-functionality activities (as the growth of Social Farms) in agriculture, also with the aim of save the biodiversity. In Italy, Social Farms are still in a pioneering phase; there are no pre-established models and the different realities of social farming, in recent years, have tried to put together some good practices referring to a personal field experience rather than coded models. The Animal Farm, social farm in Ladispoli (Rome, Italy), has developed a functional model, which is proposed in the present work, which consists in realizing together reproduction of rare breeds, educational activities and social farm activities. The innovative idea is to employ rare domestic animals in rehabilitative activities (AAA animal-assisted activities), encouraging their dissemination and preservation. The workshops are organized in daily activities where the various institutions of the territory (rehabilitation centers for disables, hospitals, etc.) bring their users (individual assets or groups) to carry out animal-assisted activities and practical workshops. The processed path type is aimed at people with medium and severe mental disabilities. The workshop is structured in two levels of activity: routine works and creative work. The experiments implemented within the AAA paths in the Animal Farm of Ladispoli (Rome, Italy) confirm the key role of animals as a facilitator in rehabilitation programs for the disabled. The Animal Farm proposes a model, repeatable in other realities, that allows the realization of animal-assisted activities with rare domestic breeds, preserving them from extinction. It is reported a preliminary study about the positive effects of using endangered races for AAA laboratories.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".