Establishing consensus on the best ways to educate children about animal welfare and prevent harm: An online Delphi study
Bibliographic record
Abstract
Abstract Many animal welfare organisations deliver education programmes for children and young people, or design materials for schoolteachers to use. However, few of these are scientifically evaluated, making it difficult for those working in this field to establish with any certainty the degree of success of their own programmes, or learn from others. There has been no guidance specifically tailored to the development and evaluation of animal welfare education interventions. Accordingly, a three-stage online Delphi study was designed to unearth the expertise of professionals working in this field and identify degree of consensus on various aspects of the intervention process: design, implementation and evaluation. Thirty-one experts participated in Round 1, representing eleven of 13 organisations in the Scottish Animal Welfare Education Forum (SAWEF), and eleven of 23 members of the wider UK-based Animal Welfare Education Alliance (AWEA). Seven further professionals participated, including four based in Canada or the US. Eighty-four percent of the original sample participated in Round 2, where a high level of consensus was apparent. However, the study also revealed areas of ambiguity (determining priorities, the need for intervention structure and degree of success). Tensions were also evident with respect to terminology (especially around cruelty and cruelty prevention), and the common goal for animal welfare to be part of school curricula. Findings were used to develop a web-based framework and toolkit to enable practitioners to follow evidence-based guidance. This should enable organisations to maximise the quality and effectiveness of their interventions for children and young people.
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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 | 0.185 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".