Exploring the Evolution and Trends in the Peri-Urban Planning: A Bibliometric Overview
Bibliographic record
Abstract
Peri-urban regions have evolved from a conceptual place and process to a heterogeneous location competing for spatial equity.Despite numerous studies on peri-urban planning, the non-judicious planning process and the lack of evidence-based decision-making remain pertinent issues in peri-urban areas.Although research on peri-urban planning is increasing, establishing standardized frameworks and facilitating cross-regional comparisons still require a widely adopted and unambiguous approach.Furthermore, there is a lack of understanding of how peri-urban areas function and the intrinsic variability within this vast domain.The paper seeks to comprehend the evolution of peri-urban areas and to suggest critical areas for sustainable peri-urban planning by employing science mapping tools to assess 622 articles from the Web of Science, which were selected from a total of 1,102 articles.According to the findings, peri-urban planning extends beyond the fundamentals of planning and now encompasses a wide range of subjects.Sustainable development concepts such as peri-urban landscapes, ecosystem services, and green infrastructure have evolved to address the complexity of urban-peri-urban networks.Using VOSviewer and SciMAT to assess article data provides significant insights into the evolution of peri-urban planning.The findings underscore the importance of evidence-based decision-making in peri-urban planning.The unclear spatial representation of sustainable development principles stresses the need for periurban planning strategies that are broadly embraced and clear.Moreover, the underrepresentation of topics such as gentrification, neoliberalism, and institutional structures indicates a knowledge gap in fully comprehending the processes and implications of peri-urban planning.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.251 | 0.316 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".