Land degradation and development policies and studies have clearly been needing an overhaul. In this introduction to a journal volume published in 1999, Batterbury and Bebbington take the time to outline how each of the papers included in the preceding volume ground themselves in multifaceted approaches with multiple levels of analysis.
Off the bat, one of the clearest issues is access to resource and how that is affected by different social structures, including local, statewide, federal, and global governance. All of these levels are intertwined and this is demonstrated in the variance of options that Batterbuy and Bebbington offer to make the papers more relevant (local political ecology vs. partnership research).
Something that occurs to me as insurmountable is how intertwined each level of analysis is to its neighbors. This is where emphasis of level really comes into play in clarifying the point or goal of research. However, where one emphasis takes precedence in a paper, report, or research, I am confused as to how one could possibly feel satisfied with results without feeling as though other levels of analysis where shortchanged. This is not to discount this type of research.
Land degradation is a serious problem to be sure, but the best way to communicate the environmental concerns associated with it is even harder. What levels of analysis are most effective? Must each report be catered to a certain group to give its full potential of understanding? How far back must historical relevance and research go to provide a satisfiable picture?
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ReplyDeleteYou've hit on the central conundrum for those of us who do multi-scaled analysis. We all agree it's important. It's just incredibly hard to do. If you are doing qualitative work, you end up not being able to explore each level with the same amount of careful ethnographic texture. In quantitative research (ie global change issues) it's a challenge to find ways to bridge the scales of analysis and 'scale' the data. I think the answer here is many researchers doing many kinds/levels of work backed with a commitment to finding ways to quantify data at broader scales (ie what do all the small case studies add up to?)