Designing a Meta-Synthesis Model of Affecting Drivers of Land Use Changes by Systematic Review of Previous Studies
Bibliographic record
Abstract
The main goal of this study is to identify the most important factors affecting land use change based on previous literature. The method used is qualitative and a type of meta-study known as meta-synthesis. We review the purpose of each study, data collection methods and research design/questions for this task. We perform a systematic search based on keywords used in articles on land use change and transformation of land in the following databases: Scopus, Springer, and Science Direct for articles from 1976 to 2015. After reviewing 63 articles out of 286 articles, we identified 5 dimensions and 28 factors as the most important indicators of land use change in various domestic and foreign studies. The results of this study show that the most important dimensions of land use change are economic, demographic and climate. The most important drivers are urban population, average rainfall, and changes in the price of agricultural land.
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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 | 0.223 | 0.389 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.036 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".