Systematic mapping and bibliometric analysis of meta-analyses on animal cognition
Bibliographic record
Abstract
Meta-analyses play an important role in empirically synthesising research and guiding future directions. The field of animal cognition is rapidly expanding, with both empirical and review papers increasing at a faster rate than those in the life sciences overall. However, the use of meta-analyses, their methodological rigour, and the geographic distribution of research activity remain unclear. We systematically reviewed 49 meta-analytical studies encompassing 1824 primary studies on animal cognition. Half of the meta-analytical studies focused on the evolution and diversity of non-human animal cognition, while the other half used animals as models to understand human cognition. Most studies addressed factors affecting cognitive abilities, focusing on mammals and birds. Although many studies aimed to examine evolutionary or diversity-related questions, few analysed cognitive variation across species or tested evolutionary hypotheses, and even fewer incorporated phylogenetic relationships. While some studies investigated sex differences, many reported that they could not due to unbalanced sex ratios in the primary studies, notably a predominance of males. Both primary and meta-analytical studies often lacked adequate methodological reporting and rarely shared raw data or analysis scripts. Our bibliometric analysis showed that research is geographically concentrated, with authorship and collaboration mostly in high-income countries. To address current gaps, we recommend greater adherence to open science practices, improved regional inclusivity, and broader taxonomic and individual-level coverage. Finally, we highlight the complementary roles of meta-analyses and Big Team Science in advancing the field by improving its transparency, inclusivity, and reliability.
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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 | 0.118 | 0.396 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.132 | 0.111 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".