The Zoo Sphere of Influence Model
Bibliographic record
Abstract
In January 2023 we proposed the Zoo Sphere of Influence, a new model to represent the value of zoos/aquariums for species and society. This model set out to replace the 50-year-old perception of zoos/aquariums as fulfilling only the objectives of conservation, education, research, and recreation, and instead demonstrate the broad range of impacts zoos/aquariums have for species and society. The Zoo Sphere of Influence Model considers zoos/aquariums working at different scales (from local to international) and acknowledges a wide range of areas that they work. In addition to a visual representation of the value of zoos/aquariums, the initial paper proposed a proof-of-concept evaluation matrix that they could use to self-assess impact. Over a fifteen-month period, we trialled and developed the proposed model and assessment matrix through a global consultation and piloting process (this included piloting at 12 zoos, 5 international workshops/demos and a feedback survey that ran for 7 months). We have updated the model to include new sections on Indigenous Peoples & Local Communities (IP & LCs); Sustainability; and Diversity, Equity, Access, and Inclusion (DEAI). We have additionally updated the assessment criteria to incorporate key zoo/aquarium documents and have added a Core Values section reflecting the fundamental standards conservation zoos/aquariums should be fulfilling. This paper presents the updated version of the Zoo Sphere of Influence. We hope that the model will help zoos/ aquariums achieve the highest possible standard of practice and provide a means of recognising and celebrating these achievements.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".