Measuring Thematic Alignment Between Journal Scope and its Published Articles

This project investigates whether articles published in a scientific journal align with the stated Aims & Scope of the journal. I propose a semantic similarity–based methodology that represents both the journal scope and article abstracts in a shared embedding space and quantifies semantic alignment between them using cosine similarity. Using PLOS Computational Biology as a case study journal, I analyze thematic coherence across the published articles, identify drifts over time and potential outlier articles.

Methodology

1. Data Collection
   • Article abstracts are retrieved from the Semantic Scholar Graph API
   • Papers are filtered by their venue and availability of abstracts
2. Sentence Embedding
   • The Aims & Scope of journal and article abstracts are encoded using a Sentence-BERT model
   • Each text is represented as a fixed-size semantic embedding
3. Alignment Score
   • Alignment is computed using cosine similarity between article embeddings and the scope embedding
   • Scores range from −1 to 1, with higher values indicating stronger thematic alignment
4. Analysis of Results
   • Distribution of alignment scores
   • Yearly trends and rolling averages
   • Inspection high and low alignment outliers qualitatively

Clone Repository

git clone https://github.com/miadalavinezhad/Thematic-Alignment.git
cd thematic-alignment

Install the Package

pip install e .

Usage

Notebooks are made for demonstration and experiment purposes. Core logics are implemented in Python modules.

from thematic_alignment import fetch_papers
from thematic_alignment import EmbeddingModel
from thematic_alignment import cosine_alignment
from thematic_alignment import yearly_alignment