Bringing the “Moral Charge” Home: Fair Trade within the North and within the South*

Abstract Fair trade is typically understood as an alternative market system that aims to right historically inequitable terms of trade between the geopolitical North and South and foster more direct producer/consumer linkages. We suggest that a more expansive application of the term “fair trade” to encompass agro‐food initiatives within the North and South has considerable analytic and practical utility. We profile five such initiatives in the United States and two in Mexico. The U.S. undertakings are best understood as “proto‐” fair trade projects that frame their work principally as an effort to preserve “family farming” rather than as an exercise to achieve fairness in the marketplace. The Mexican initiatives more explicitly embrace the certification‐criteria‐labeling model of international fair trade. Both, we conclude, hold potential to harness fair trade's “moral charge” to improve conditions for small producers and laborers in North and South experiencing most directly the negative effects of economic globalization.     

Multiple-Scale Integrated Analysis of Farming Systems: The Thuong Lo Commune (Vietnamese Uplands) Case Study

This paper presents an application of an innovative methodological tool for the integrated assessment of farming systems (Multi-Objective Multiple-Scale Integrated Representation). It describes the effect of rural development in parallel on economic and ecological reading at different levels: household, village, and the commune comprising 3 villages. Socio-economic and environmental indicators are organized into integrated packages across scales. This is an ex-post facto analysis of a program of rural development implemented in the Central Vietnamese uplands in 1995 by the central government with the collaboration of FAO. We conclude that two classic problems can only be faced by using an integrated analysis across levels: Large scale generalizations can miss important location-specific characteristics, and exclusive focus on location specific issues carries the risk of loosing the big picture.     

Theory-based evaluation of a comprehensive Latino education initiative: an interactive evaluation approach

Latino student access to higher education has received significant national attention in recent years. This article describes a theory-based evaluation approach used with ENLACE of Hillsborough, a 5-year project funded by the W.K. Kellogg Foundation for the purpose of increasing Latino student graduation from high school and college. Theory-based evaluation guided planning, implementation as well as evaluation through the process of developing consensus on the Latino population of focus, adoption of culturally appropriate principles and values to guide the project, and identification of strategies to reach, engage, and impact outcomes for Latino students and their families. The approach included interactive development of logic models that focused the scope of interventions and guided evaluation designs for addressing three stages of the initiative. Challenges and opportunities created by the approach are discussed, as well as ways in which the initiative impacted Latino students and collaborating educational institutions.     

Subjective machines: Probabilistic risk assessment based on deep learning of soft information

For several years machine learning methods have been proposed for risk classification. While machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used for probabilistic risk assessment. Probabilistic risk assessment is a subjective process. The problem of how well machine learning methods can emulate expert judgments is challenging. Expert judgments are based on mental shortcuts, heuristics, which are susceptible to biases. This paper presents a process for developing natural language-based probabilistic risk assessment models, applying deep learning algorithms to emulate experts’ quantified risk estimates. This allows the risk analyst to obtain an a priori risk assessment when there is limited information in the form of text and numeric data. Universal sentence embedding (USE) with gradient boosting regression (GBR) trees trained over limited structured data presented the most promising results. When we apply these models’ outputs to generate survival distributions for autonomous systems’ likelihood of loss with distance, we observe that for open water and ice shelf operating environments, the differences between the survival distributions generated by the machine learning algorithm and those generated by the experts are not statistically significant.     

Computing the <Emphasis Type="Italic">k</Emphasis>-resilience of a synchronized multi-robot system

The vertex arboricity va(G) of a graph G is the minimum number of colors the vertices can be colored so that each color class induces a forest. It was known that \(va(G)\le 3\) for every planar graph G. In this paper, we prove that \(va(G)\le 2\) if G is a planar graph without intersecting 5-cycles.     

Mapping Discrimination Experienced by Indonesian Trans* FtM Persons

This work sought to document how Indonesian trans* FtM persons experienced discrimination across the interlinked domains of social networks, religious and educational institutions, employment and the workplace, and health care institutions. Objectives were (1) to map the discrimination experienced by trans* FtM individuals in Indonesia, and (2) to establish the specific priorities of the Indonesian trans* FtM community. In-depth interviews, focus groups, and participant observation was used involving 14 respondents. Findings revealed that respondents experienced othering through rejection, misidentification, harassment, “correction,” and bureaucratic discrimination across the five preestablished domains. Health care and a lack of information emerged as areas of particular concern for respondents. This work calls for health care that is sensitive to the needs of trans* FtM people coupled with high-quality information to alleviate the cycles through which discrimination is sustained.     

Visions of resilience: lessons from applying a digital democracy tool in New York’s Jamaica Bay watershed

Resilience to extreme weather events and other sudden changes is an issue facing many communities in the early twenty-first century. Planning to respond to disasters is particularly complicated in densely inhabited, multi-jurisdictional urban social-ecological systems like the watershed of Jamaica Bay, a large urbanized estuary on the south side of New York City. This area contains parklands managed by New York City, the National Park Service, and other agencies, four sewage treatment plants, three former landfills, and urban and suburban communities, all of which were heavily impacted by Hurricane Sandy in 2012. Here successful resilience planning and response requires participation from a wide variety of government and civil society players each with different types of knowledge, value systems, and expectations about what resilience means. To investigate how “visions” of future resilience differed among several communities living in or concerned with Jamaica Bay, New York, we deployed a free, Internet-based modeling framework called Visionmaker that enabled interactive scenario creation and testing. Through a series of standardized workshops, we recruited participants from a variety of different communities of practice (i.e. researchers, land managers, educators, non-governmental organization staff, and community board members) to design “visions of resilience”. Visions spanned terrestrial and marine environments and contained natural and built ecosystems. Most users favored increasing resilience through expanding salt marsh and green infrastructure while, for the most part, keeping the built city landscape of streets and buildings intact. We compare and contrast these visions and discuss the implications for future resilience planning in coastal cities.