Choices of Research Methodologies on Climate Change Adaptation Especially Focusing on Agriculture Sector: A Systematic Review
Bibliographic record
Abstract
A rapid increase in climate researches by applying diverse methodologies and approaches in recent decades. These researches have directly or indirectly contributed in better understanding of climate issues, risks and vulnerabilities. It has improved awareness and capacities of the public and communities to adapt to the vulnerabilities and impacts. It, further, contributes in formulation of climate policies and plans to address climate risks and vulnerabilities at the local and national levels. Appropriate methodologies lead to better results in the researches. This paper has applied systematic review of the published papers (2010 -2017) to understand the general and specific research methodologies in climate discourse especially in Web of Science (WS), Springer Link (SL) and Science Direct (SD). Altogether, 37 journal papers (10 WS, 13 SL and 14 SD) were selected for the detail analysis based on the assessment of abstracts, which was mainly concentrated on research methodologies specializing in agriculture. In the process, the authors have analyzed the contents, research methodologies, data analysis, and geographical coverages. The analysis, further, concentrated on the scope and limitations of the research methodologies used. Wide-ranging research methodologies are found that are applied by the researchers in the climate change discourse. Some researchers have applied general research methodologies whereas others have used specific research methodologies and model analysis. Furthermore, it is comprehended that the combination of research methodologies and approaches through focus group discussion together with household survey and model analysis is the effective way for the research by using quantitative and qualitative data.
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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 | 0.076 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.035 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".