Marine protected area and climate change: A mapping review
Bibliographic record
Abstract
This comprehensive scientometric analysis, utilizing CiteSpace and data from the Web of Science Core Collection, examines the trajectory of research on Marine Protected Areas (MPAs) in the context of climate change. Analysing 2782 articles and 117,904 cited references, the study observes a significant surge in publications between 2019 and 2023, with Australia, England and Canada as leading contributors. Our findings reveal key conceptual pillars such as ‘marine protected areas’, ‘climate change’, ‘conservation’, ‘management’, and ‘biodiversity’. The research domain is characterized by 10 major co-citation clusters, with a notable focus on “coral reefs”, “temperature-driven coral decline”, and “large MPAs”. The increasing citation frequency during 2020–2023, particularly in clusters related to coral reefs and regional studies, signals a heightened global awareness of MPAs' role in mitigating climate change impacts. This review provides essential insights, informing future directions for both academic research and policymaking in marine conservation amid ongoing climatic changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".