The changes in zoological publication rates and focal subdisciplines between 1960 and 2022
Bibliographic record
Abstract
Abstract Since ancient times, zoology, as the branch of biology dealing with animals, has been a cornerstone of natural science and has developed substantially over the last century. We conducted a bibliometric analysis using structural topic modeling (STM) to determine changes in the representation of principal zoological subdisciplines in the literature between 1960 and 2022. We collated a corpus of 217 414 articles from 88 top‐ranked zoology journals and identified three main fields: (i) ecology, (ii) evolution, and (iii) applied research. Within these, we identified 10 major subdisciplines. The number of studies published per year grew from 118 in 1960 to 6635 in 2022. Macroscale‐related subdisciplines increased while classical and traditional subdisciplines decreased. Mammals (34.4%) and insects (18.1%) were the dominant taxa covered, followed by birds (15.2%) and fish (8.0%). Research on mammals, insects, and fish involved a broad range of subdisciplines, whereas studies of birds focused on ecological subdisciplines. Most publications were from the United States, followed by the United Kingdom, Germany, Canada, Australia, China, and Japan, with two developing countries, China and South Africa among the top 15 countries. There were different subdiscipline biases between countries, and the gross domestic product of each country correlated positively with its publication output ( R 2 = 0.681). We discuss our findings in the context of advances in technological innovations and computing power, as well as the emergence of ecology as a formal sister discipline, driven by changing environmental pressures and societal values. We caution that valuable publications from traditional zoological fields must not be completely supplanted by more contemporary topics and increasingly sophisticated analyses.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.075 | 0.089 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".