2021
DOI: 10.1111/aec.13052
|View full text |Cite
|
Sign up to set email alerts
|

What's hot and what's not – Identifying publication trends in insect ecology

Abstract: Research disciplines in science have historically developed in silos but are increasingly multidisciplinary. Here, we assessed how the insect ecology literature published in ecological and entomological journals has developed over the last 20 years and which topics have crossed discipline boundaries. We used structural topic modelling to assess research trends from 34 304 articles published in six ecology journals and six entomology journals between 2000 and 2020. We then identified and compared topics that em… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
12
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8

Relationship

4
4

Authors

Journals

citations
Cited by 12 publications
(12 citation statements)
references
References 59 publications
0
12
0
Order By: Relevance
“…To determine the main topics in our corpus, we used structural topic modelling (STM) to analyse abstracts and titles using the ‘stm' package (Roberts et al 2019) in R. When fitting a topic model, the researcher must specify the number of topics to find in the corpus. After trialling 60 and 20 as the number of topics, we chose 30 topics; a number that we judged to be enough to provide a good overview of the corpus whilst not being too large to interpret (Andrew et al 2021).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…To determine the main topics in our corpus, we used structural topic modelling (STM) to analyse abstracts and titles using the ‘stm' package (Roberts et al 2019) in R. When fitting a topic model, the researcher must specify the number of topics to find in the corpus. After trialling 60 and 20 as the number of topics, we chose 30 topics; a number that we judged to be enough to provide a good overview of the corpus whilst not being too large to interpret (Andrew et al 2021).…”
Section: Methodsmentioning
confidence: 99%
“…Topic modelling, a type of machine learning, is unsupervised and does not impose topics as identified by authors. This approach allows research categories to emerge that may not be revealed by traditional meta‐analyses and systematic reviews (Westgate et al 2015, Andrew et al 2021, Evans et al 2021a). Through post‐hoc analyses of the fitted model, topic modelling allows researchers to interrogate topic popularity, trajectory, similarity and generality.…”
Section: Introductionmentioning
confidence: 99%
“…We hope to incentivize potential authors to assess the publication trends over the last 2 years and identify opportunities in their research for future publication in Austral Ecology. Our study adds to the rich datasets that we have collected on the topic popularity (Westgate et al, 2020) and manuscript rejections (Andrew, 2020) in Austral Ecology, as well as publication trends more broadly (Andrew et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, invertebrate biodiversity continues to diminish in urban settings [24], a loss primarily abetted by apathy toward this important component of ecosystems. Whereas ecology journals have shown an increase in studies of community ecology, including in urban settings, the term "urban" did not emerge as important in entomological journals [25].…”
Section: Introductionmentioning
confidence: 99%