Since first being introduced in the mid 1990s, the term "citizen science"-the intentional engagement of the public in scientific research-has seen phenomenal growth as measured by the number of projects developed, people involved, and articles published. In addition to contributing to scientific knowledge, many citizen science projects attempt to achieve learning outcomes among their participants, however, little guidance is available for practitioners regarding the types of learning that can be supported through citizen science or the measuring of learning outcomes. This study provides empirical data to understand how intended learning outcomes first described by the informal science education field have been employed and measured within the citizen science field. We also present a framework for describing learning outcomes that should help citizen science practitioners, researchers, and evaluators in designing projects and in studying and evaluating their impacts. This is a first step in building evaluation capacity across the field of citizen science. Knowledge, Awareness, Understanding: Measurable demonstration of assessment of, change in, or exercise of awareness, knowledge, understanding of a particular scientific topic, concept, phenomena, theory, or careers central to the project. Strand (2), Understanding: Come to generate, understand, remember, and use concepts, explanations, arguments, models, and facts related to science. Engagement, interest or motivation in science: Measurable demonstration of assessment of, change in, or exercise of engagement/interest in a particular scientific topic, concept, phenomena, theory, or careers central to the project. Strand (1), Interest and motivation: Experience excitement, interest and motivation to learn about phenomena in the natural and physical world. Skills related to science inquiry: Measurable demonstration of the development and/or reinforcement of skills, either entirely new ones or the reinforcement, even practice, of developing skills. Strand (3), Science Exploration: Manipulate, test, explore, predict, question, and make sense of the natural and physical world; and Strand (5): Participate in scientific activities and learning practices with others, using scientific language and tools Attitudes toward science: Measurable demonstration of assessment of, change in, or exercise of attitude toward a particular scientific topic, concept, phenomena, theory, or careers central to the project or one's capabilities relative to these areas. Attitudes refer to changes in relatively stable, more intractable constructs such as empathy for animals and their habitats, appreciation for the role of scientists in society or attitudes toward stem cell research. Related to Strand (6), Identity: Think about themselves as science learners, and develop an identity as someone who knows about, uses, and sometimes contributes to science. Also, related to Strand (4), Reflection: Reflect on science as a way of knowing; on processes, concepts, and institutions of science; and on t...

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We advance a theory of resilience as it applies to the challenges of international development. The conceptualization we advance for development resilience focuses on the stochastic dynamics of individual and collective human well-being, especially on the avoidance of and escape from chronic poverty over time in the face of myriad stressors and shocks. Development resilience clearly nests within it the related but distinct idea of humanitarian resilience and thereby offers a conceptual apparatus to integrate the humanitarian and development ambitions. We discuss the implications for programming, systems integration, and measurement.sustainability | vulnerability | poverty traps | risk A s climate change, political instability, and economic volatility appear to many observers to have become more pronounced, the risks faced by many of the world's poor seem to have become more intense and less predictable. In search of a strategic response to such risks, international development and humanitarian organizations have manifested a sharp increase in interest in the concept of "resilience." Given that resilience is fast becoming a distinct policy objective, we need a clear theory of development resilience to guide measurement and programming and to inform evaluation. We offer a theory of resilience as it applies to the challenges of international development. The conceptualization we advance for "development resilience" focuses on the stochastic dynamics of individual and collective human well-being, in particular the capacity to avoid and escape from unacceptable standards of living-"poverty," for short-over time and in the face of myriad stressors and shocks. Our aim here is to lay the groundwork necessary to inform more precise use of the resilience concept, to articulate better theories of change, and to promote more focused measures of resilience for development applications.

Although the use of qualitative methods has increased greatly in popularity, many still question the defensibility of the qualitative orientation. It is argued here that questions concerning the credibility and status of qualitative inquiry are related to the privatization of qualitative analysis. The particular area of qualitative analysis I focus on is the process of category development. It is my argument that qualitative researchers must make all aspects of their analysis open to public inspection. In order to achieve this objective, I propose a two-dimensional model designed to facilitate the documentation of procedures used to generate categories. The domain representing the first dimension specifies the various components or actions associated with the development of categories. The second domain addresses the temporal aspects of category development. The intersection of these two analytical domains forms a two-dimensional table that may be used to document the nature of the analytical actions employed in a given study. An empirical example is presented with the intention of illustrating the utility of the approach. The implications of this approach, and some possible criticisms, are considered. 3 The frequent absence of specificity and detail in data analysis procedures should not be seen as something peculiar to the qualitative orientation. In the case of multiple regression analysis, for example, investigators commonly neglect to provide detailed information on underlying assumptions (e.g., absence of specification error, linearity, homoscedasticity, multicollinearity, etc.). It so happens that the absence of detail and specificity is less conspicuous, though no less serious, in the case of qualitative analysis. 254