Unveiling two decades of environmental policy research trends: topic modeling-based machine learning insights
Bibliographic record
Abstract
Purpose The study aims to analyze and report on key research trends within the environmental policy (EPOL) discipline, focusing on identifying important topic areas and highlighting the theoretical concepts and analysis methods that policy scholars should prioritize. By exploring these aspects, the study seeks to enhance policy effectiveness in addressing environmental challenges. Design/methodology/approach The study uses a content analytics approach, employing the Latent Dirichlet Allocation (LDA) model to identify research hotspots. The LDA model analyzes research trends across 2 decades (2000–2019), based on a dataset of 33,683 abstracts from 30 peer-reviewed journals focused on EPOL research. This methodology enables a comprehensive examination of emerging topics within the discipline. Findings The analysis identifies 40 significant research topics within the EPOL literature. Key findings highlight the increasing focus on niche areas such as climate change resilience, food security, renewable energy, urban spatial planning and ecosystem services. These trends reflect a shift towards more specialized and targeted policy issues within the broader field of EPOL. Originality/value This study provides a novel contribution to EPOL scholarship by offering a quantitative, data-driven analysis of research trends over the past 2 decades. The use of the LDA model for profiling research hotspots introduces a new perspective on how to systematically track and synthesize emerging EPOL topics. The findings have the potential to inform future research and policy development by fostering a more integrative understanding of the field.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".