Credible impact evaluation in complex contexts:Confirmatory and exploratory approaches

Debate continues over how best international development agencies can evaluate the impact of actions intended to reduce poverty, insecurity and vulnerability in diverse and complex contexts. There are strong ethical grounds for simply asking those intended to benefit what happened to them, but it is not obvious how to do so in a way that is sufficiently free from bias in favour of confirming what is expected. This article considers scope for addressing this problem by minimizing the prior knowledge participants have of what is being evaluated. The tensions between more confirmatory and exploratory methodological approaches are reviewed in the light of experience of designing and piloting a qualitative impact assessment protocol for evaluating NGO interventions in complex rural livelihood transformations. The article concludes that resolving these tensions entails using mixed methodologies, and that the importance attached to exploratory (nested within confirmatory) approaches depends on contextual complexity, the type of evidence sought and the level of trust between stakeholders. </jats:p

Detecting modification of biomedical events using a deep parsing approach

<p>Abstract</p> <p>Background</p> <p>This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. <it>analysis of IkappaBalpha phosphorylation</it>, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. <it>inhibition of IkappaBalpha phosphorylation</it>, where phosphorylation did <it>not </it>occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser.</p> <p>Method</p> <p>To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the <it>RASP </it>parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm.</p> <p>Results</p> <p>Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features.</p> <p>Conclusions</p> <p>Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.</p

Why did I not prepare for this? The politics of negotiating fieldwork access, identity, and methodology in researching microfinance institutions

It has been increasingly recognized that undertaking qualitative research can pose many challenges for researchers. However, scanty literature focuses directly on the experiences of doctoral research students from developing countries studying in Western Europe and other similar geographic regions, and the challenges of doing fieldwork when they return “back home”. In this article, I use my experiences in the process of undertaking PhD fieldwork on two donor-funded microfinance institutions located in Zambia to demonstrate that doctoral students from specific regions (Africa in particular) undertaking research in their native countries can struggle to manage and make sense of the challenges and identity issues raised in their “familiar” environments. I also present a detailed discussion of how various gatekeepers and participants facilitated access, identity alteration, and the impact of insider–outsider positionality on collected data. It is concluded that organizational “politics” and local context can have significant bearing on power relationships, identities of researchers, and methodological preferences

Financing micro-entrepreneurs for poverty alleviation: a performance analysis of microfinance services offered by BRAC, ASA, and Proshika from Bangladesh

Microfinance services have emerged as an effective tool for financing microentrepreneurs to alleviate poverty. Since the 1970s, development theorists have considered non-governmental microfinance institutions (MFIs) as the leading practitioners of sustainable development through financing micro-entrepreneurial activities. This study evaluates the impact of micro-finance services provided by MFIs on poverty alleviation. In this vein, we examine whether microfinance services contribute to poverty alleviation, and also identify bottlenecks in micro-finance programs and operations. The results indicate that the micro-loans have a statistically significant positive impact on the poverty alleviation index and consequently improve the living standard of borrowers by increasing their level of income