Quantifying the contribution of zoos and aquariums to peer-reviewed scientific research
Bibliographic record
Abstract
Modern zoos and aquariums aspire to contribute significantly to biodiversity conservation and research. For example, conservation research is a key accreditation criterion of the Association of Zoos and Aquariums (AZA). However, no studies to date have quantified this contribution. We assessed the research productivity of 228 AZA members using scientific publications indexed in the ISI Web of Science (WoS) database between 1993 and 2013 (inclusive). AZA members published 5175 peer-reviewed manuscripts over this period, with publication output increasing over time. Most publications were in the zoology and veterinary science subject areas, and articles classified as “biodiversity conservation” by WoS averaged 7% of total publications annually. From regression analyses, AZA organizations with larger financial assets generally published more, but research-affiliated mission statements were also associated with increased publication output. A strong publication record indicates expertise and expands scientific knowledge, enhancing organizational credibility. Institutions aspiring for higher research productivity likely require a dedicated research focus and adequate institutional support through research funding and staffing. We recommend future work build on our results by exploring links between zoo and aquarium research productivity and conservation outcomes or uptake.
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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 | 0.077 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".