“AWECoP has made my teaching experience so much better!” – Creating community and improving teaching practice through an Animal Welfare Education Community of Practice (AWECoP)
Bibliographic record
Abstract
Abstract An animal welfare education community of practice (AWECoP) for those teaching animal welfare science, applied ethology, and/or animal ethics was created to develop a dialogue amongst educators within the field of animal welfare science. The purpose of this paper is to describe the history, objectives, and members’ experiences within this community. AWECoP hosts 6–8 meetings annually for members to discuss topics relevant to our community and exchange teaching resources; within its first two years, the community has grown to 121 members representing approximately 70 institutions across six continents. A 12-question, mixed-method survey was distributed to capture member demographics, engagement with AWECoP, motivations for joining, and self-evaluation of AWECoP’s impacts. Quantitative data from the survey are presented descriptively, while reflexive thematic analysis was applied to the qualitative data. Survey respondents (n = 54) felt that AWECoP is a vital community and safe space for members to share their ideas and receive feedback, inspiration, information, and resources regarding subject-specific and broader pedagogical topics. As a result, a majority experienced professional (e.g. development of new contacts) and personal (e.g. increased feeling of belonging in their field) benefits, as well as impacts realised in their teaching practice. We conclude with an examination of challenges faced in ensuring AWECoP remains accessible to a growing membership and offer recommendations for facilitating similar communities to support fellowship and training in the teaching of animal welfare and related disciplines.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".