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  1. Abstract

    Development projects present ambiguous ethical terrain for anthropologists to navigate. Particularly in relation to WaSH (Water, Sanitation, Hygiene) infrastructures which mediate human and environmental health. Our interdisciplinary team of anthropologists and engineers initially set out to design context‐sensitive on‐site wastewater treatment infrastructures for homes along Belize's Placencia Peninsula. The project's beginning coincided with the announcements of a government sponsored centralized wastewater infrastructure project and the construction of a cruise ship port on a nearby island, however. Soon the wastewater project's promises—economic opportunity, improved human and environmental health, modernization ‐ came crashing into its pratfalls—exacerbating existing inequalities, loss of livelihoods, and diminished local governance. Our team was left with uncertain decisions about how to engage with improving infrastructure, given the emerging community dynamics. By detailing the imperfect trade‐offs at play, we highlight ethical complexities inherent when communities’ development futures are at stake. Anthropology's fraught history includes legacies of unintended harms from entanglement in others’ inequities. However, avoiding involvement out of excessive caution risks leaving marginalized voices unheard and extant problems unresolved. This case immersed our team in the inherent optimism and ethical experimentation which underlie development contexts. Our analysis adopts the structure from Whiteford and Trotters’ (2008) “Ethical‐Problem Solving Guide” to reveal the layered tensions that underly critical WaSH infrastructures.

     
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  2. null (Ed.)
    Life cycle assessment (LCA), a tool used to assess the environmental impacts of products and processes, has been used to evaluate a range of aquaculture systems. Eighteen LCA studies were reviewed which included assess- ments of recirculating aquaculture systems (RAS), flow-through systems, net cages, and pond systems. This re- view considered the potential to mitigate environmental burdens with a movement from extensive to intensive aquaculture systems. Due to the diversity in study results, specific processes (feed, energy, and infrastructure) and specific impact categories (land use, water use, and eutrophication potential) were analyzed in-depth. The comparative analysis indicated there was a possible shift from local to global impacts with a progression from extensive to intensive systems, if mitigation strategies were not performed. The shift was partially due to increased electricity requirements but also varied with electricity source. The impacts from infrastructure were less than 13 % of the environmental impact and considered negligible. For feed, the environmental impacts were typically more dependent on feed conversion ratio (FCR) than the type of system. Feed also contributed to over 50 % of the impacts on land use, second only to energy carriers. The analysis of water use indicated intensive recirculating systems efficiently reduce water use as compared to extensive systems; however, at present, studies have only considered direct water use and future work is required that incorporates indirect and consumptive water use. Alternative aquaculture systems that can improve the total nutrient uptake and production yield per material and energy based input, thereby reducing the overall emissions per unit of feed, should be further investigated to optimize the overall of aquaculture systems, considering both global and local environmental impacts. While LCA can be a valuable tool to evaluate trade-offs in system designs, the results are often location and species specific. Therefore, it is critical to consider both of these criteria in conjunction with LCA results when developing aquaculture systems. 
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  3. null (Ed.)
    Communities of color are disproportionately burdened by environmental pollution and by obstacles to influence policies that impact environmental health. Black, Hispanic, and Native American students and faculty are also largely underrepresented in environmental engineering programs in the United States. Nearly 80 participants of a workshop at the 2019 Association of Environmental Engineering and Science Professors (AEESP) Research and Education Conference developed recommendations for reversing these trends. Workshop participants identified factors for success in academia, which included adopting a broader definition for the impact of research and teaching. Participants also supported the use of community-based participatory research and classroom action research methods in engineering programs for recruiting, retaining, and supporting the transition of underrepresented students into professional and academic careers. However, institutions must also evolve to recognize the academic value of community-based work to enable faculty, especially underrepresented minority faculty, who use it effectively, to succeed in tenure promotions. Workshop discussions elucidated potential causal relationships between factors that influence the co-creation of research related to academic skills, community skills, mutual trust, and shared knowledge. Based on the discussions from this workshop, we propose a pathway for increasing diversity and community participation in the environmental engineering discipline by exposing students to community-based participatory methods, establishing action research groups for faculty, broadening the definition of research impact to improve tenure promotion experiences for minority faculty, and using a mixed methods approach to evaluate its impact. 
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  4. In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental health hazards in Barbados for the period 2014 - 2018. Then, the binary classification was used to categorize each tweet as relevant or irrelevant. Next, the multiclass classification was then used to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. Results indicate that (combination of TF-IDF, psychometric, linguistic, sentiment and Twitter-specific features) using a Random Forest algorithm is the best feature for detecting and predicting binary classification with (87% F1 score). For multi-class classification, TF-IDF using Decision Tree algorithm was the best with (74% F1 score). 
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