![]() In line with scholars who stress the need for context-specific interventions, we propose a diagnostic approach to African institutional contexts and public administration. We then contrast these findings with land- and food-related development aid programmes now and in the past, and distinguish three ways in which these programmes have dealt with African systems of public administration: 1) by aligning with public administration in the donor country instead of with public administration in the recipient country, 2) by blueprinting administrative ways for African countries to work that stem from contexts alien to the African context, 3) by completely ignoring the role of administration in land use and food production.Ĭoncluding that these three approaches have not led to satisfactory results in Africa, we propose a different approach to dealing with African institutional contexts, and especially African public administration systems. The diversity in administrative styles in Africa underlines Huisman et al.’s (2016) findings and points to a need for international donor communities to focus on, and cooperate with, African public administration if the donors’ objective is to promote more sustainable land use and food production. On the basis that governance could be a key factor explaining the large variability in trends (Huisman et al., 2016), this report examines: 1) the basic characteristics of governance, institutions, and public administration in general 2) how institutions and public administration matter for governing land and food production 3) the specific African characteristics of institutions and African countries’ relatively weakly developed, though diverse, systems of public administration 4) the implications of these diverse African institutional contexts for land use and food production.įrom a review of the role of governance, institutions, and public administration in the context of African land use and food production, we conclude that African systems of public administrations (bureaucracies) in particular have a (potentially) large role to play in land-use management and food production. To learn more, see the privacy policy.This report builds on earlier PBL studies that highlight the large variability in national trends in food production and land-use change in Africa (Huisman, Vink, & van Eerdt, 2016). Special thanks to the contributors of the open-source code that was used in this project: Elastic Search, WordNet, and note that Reverse Dictionary uses third party scripts (such as Google Analytics and advertisements) which use cookies. The definitions are sourced from the famous and open-source WordNet database, so a huge thanks to the many contributors for creating such an awesome free resource. In case you didn't notice, you can click on words in the search results and you'll be presented with the definition of that word (if available). For those interested, I also developed Describing Words which helps you find adjectives and interesting descriptors for things (e.g. So this project, Reverse Dictionary, is meant to go hand-in-hand with Related Words to act as a word-finding and brainstorming toolset. That project is closer to a thesaurus in the sense that it returns synonyms for a word (or short phrase) query, but it also returns many broadly related words that aren't included in thesauri. I made this tool after working on Related Words which is a very similar tool, except it uses a bunch of algorithms and multiple databases to find similar words to a search query. So in a sense, this tool is a "search engine for words", or a sentence to word converter. It acts a lot like a thesaurus except that it allows you to search with a definition, rather than a single word. The engine has indexed several million definitions so far, and at this stage it's starting to give consistently good results (though it may return weird results sometimes). For example, if you type something like "longing for a time in the past", then the engine will return "nostalgia". It simply looks through tonnes of dictionary definitions and grabs the ones that most closely match your search query. The way Reverse Dictionary works is pretty simple.
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