Is it Egalitarianism or Enterprise Strategy? Exploring a New Method of Innovation in Open Source

This research article explores a new way of innovation found in open source communities. No longer is innovation closed where research and development is kept internal to the firm. Instead, it is becoming more open, where ideas, inventions, and intellectual property are readily traded in a global marketplace. Our research observed open source communities as something different from the received view on open source. We observed open source communities as highly organized platforms for strategic innovation where profit-seeking firms are actively involved in governance, strategic direction, and technology development. We explore the evolving relationship between firms and communities and provide insight into how these communities are organized. Our research depicts Open Innovation and open source in a new light – Federated Innovation – where open source communities are now acting as platforms to drive for strategic innovation._x000D_ This work has been funded through the National Science Foundation VOSS-IOS Grant: 1122642

Design history: exploring corporate communities

A design history is a narrative involving a multitude of social groups, interpretive flexibility, and eventual stabilization of shared understanding. Design history surfaces the practices that help shape and define engagements and can increase not only our theoretical understanding of what design is, but also our capacity to realize this understanding in practice. We use a design history perspective to examine how corporate technology initiatives establish and support open source communities and the crafting of relevant design practices that enable their advancement. We foster an evolving expression of design research that treats artifacts not as stable objects to be singularly evaluated, but as evolving systems contingent on historical trajectories

Tapestries of Innovation: Structures of Contemporary Open Source Project Engagements

Since the origins of the free-software movement, open source projects have fostered an environment for innovative ideas that has transformed much of our understanding of technology in everyday life. In our quest to learn more about the structures of large-scale contemporary open source engagements, we examine three open source networks as part of an ongoing field study (Van Maanen, 2011). We explore the innovation networks described by Lyytinen, Yoo, & Boland (2016) and resolve whether any of the open source innovative networks that we have been studying can be classified as Project, Clan, Federated, or Anarchic networks. We examine two collaborative open source projects (SPDX and OpenMAMA) housed at the Linux Foundation, and determine that they correspond to the Federated and Project innovation networks respectively. Further, we determined that the Linux Foundation itself, as an organization that houses numerous open source projects, did not fit any of the four types of networks. We therefore propose and authenticate a fifth type of network that we characterize as a Tapestry innovation network, which can illuminate the Linux Foundation’s complexity of horizontal “weft threads” of participating organizations with the vertical, less visible “warp threads” of responsibilities and endeavors. Our study reveals important implications for research and practice by challenging the accepted view of open source projects, which still largely regards engagement around loosely structured groups of volunteers working on publicly available software. It also reveals that foundations are playing increasingly strategic roles in creating and stabilizing open source projects

Human Trafficking by the Numbers: The Initial Benchmark of Prevalence and Economic Impact for Texas

Prevalence HighlightsCurrently, there are approximately 79,000 minor and youth victims of sex trafficking in Texas.Currently, there are approximately 234,000 workers who are victims of labor trafficking in Texas.Currently, there arean estimated 313,000 victims of human trafficking in Texas.Cost HighlightsMinor and youth sex trafficking costs the state of Texas approximately $6.6 billion. Traffickers exploit approximately$600 million from victims of labor trafficking in Texas.BackgroundThough human trafficking is widespread in geographically large states with large urban centers like Texas, the true scope of this hidden crime is largely unconfirmed as data on human trafficking are difficult to ascertain. Existing data gathered in anti-trafficking efforts focus almost exclusively on identified victims, shedding light on only a fraction of the problem. The first phase of the Statewide Human Trafficking Mapping Project of Texas focused on providing empiricallygrounded data as a benchmark about the extent of human trafficking across the state. The following three primary research questions guided our data collection efforts, which included queries of existing databases, interviews, focus groups, and web-based surveys.1.What is the prevalence of human trafficking in Texas?2.What is the economic impact of human trafficking in Texas?3.What is our understanding of human trafficking in Texas?MethodsThe findings in this report were derived using a multi-methods approach to quantify the prevalence and economic impact of human trafficking in Texas. Higher-than-average risk industry and community segments were chosen for sex and labor markets. We defined community segments asgroups of people considered to be at higher-than-average risk of trafficking because of risk indicators found in trafficking cases (e.g. homelessness). More specifically, rather than attempting to establish prevalence of trafficking among the 27.4 million people living in Texas, for the purposes of demonstrating our methodology, establishing some benchmarks on human trafficking prevalence and economic impact estimates, and providing a concrete example of our planned activities moving forward, victimization rates were applied to a select few community segments that are at higher-than-average risk of trafficking.The methodology has addressed the critical industry and community segments to accurately estimate prevalencewhile reducing overlap between the chosen segments

On annotation of the textual contents of Scottish legal instruments

We thank the funding from the University of Aberdeen’s Impact, Knowledge Exchange, and Commercialisation Award for this 10 week study. This work was also supported by the French National Research Agency (ANR-10-LABX-0083) in the context of the Labex EFL. We also thank the student staff: A. Andonov, A. Faulds, E. Onwa, L. Schelling, R. Stoyanov, and O. Toloch.Publisher PD

Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17

Survivorship and Growth in Staghorn Coral (Acropora cervicornis) Outplanting Projects in the Florida Keys National Marine Sanctuary

Significant population declines in Acropora cervicornis and A. palmata began in the 1970s and now exceed over 90%. The losses were caused by a combination of coral disease and bleaching, with possible contributions from other stressors, including pollution and predation. Reproduction in the wild by fragment regeneration and sexual recruitment is inadequate to offset population declines. Starting in 2007, the Coral Restoration Foundation™ evaluated the feasibility of outplanting A. cervicornis colonies to reefs in the Florida Keys to restore populations at sites where the species was previously abundant. Reported here are the results of 20 coral outplanting projects with each project defined as a cohort of colonies outplanted at the same time and location. Photogrammetric analysis and in situ monitoring (2007 to 2015) measured survivorship, growth, and condition of 2419 colonies. Survivorship was initially high but generally decreased after two years. Survivorship among projects based on colony counts ranged from 4% to 89% for seven cohorts monitored at least five years. Weibull survival models were used to estimate survivorship beyond the duration of the projects and ranged from approximately 0% to over 35% after five years and 0% to 10% after seven years. Growth rate averaged 10 cm/year during the first two years then plateaued in subsequent years. After four years, approximately one-third of surviving colonies were ≥ 50 cm in maximum diameter. Projects used three to sixteen different genotypes and significant differences did not occur in survivorship, condition, or growth. Restoration times for three reefs were calculated based on NOAA Recovery Plan (NRP) metrics (colony abundance and size) and the findings from projects reported here. Results support NRP conclusions that reducing stressors is required before significant population growth and recovery will occur. Until then, outplanting protects against local extinction and helps to maintain genetic diversity in the wild

Uninsured migrants: Health insurance coverage and access to care among Mexican return migrants

Background: Despite an expansive body of research on health and access to medical care among Mexican immigrants in the United States, research on return migrants focuses primarily on their labor market mobility and contributions to local development. Objective: Motivated by recent scholarship that documents poor mental and physical health among Mexican return migrants, this study investigates return migrants' health insurance coverage and access to medical care. Methods: I use descriptive and multivariate techniques to analyze data from the 2009 and 2014 rounds of Mexico's National Survey of Demographic Dynamics (ENADID, combined n = 632,678). Results: Analyses reveal a large and persistent gap between recent return migrants and nonmigrants, despite rising overall health coverage in Mexico. Multivariate analyses suggest that unemployment among recent arrivals contributes to their lack of insurance. Relative to nonmigrants, recently returned migrants rely disproportionately on private clinics, pharmacies, self-medication, or have no regular source of care. Mediation analysis suggests that returnees' high rate of uninsurance contributes to their inadequate access to care. Contribution: This study reveals limited access to medical care among the growing population of Mexican return migrants, highlighting the need for targeted policies to facilitate successful reintegration and ensure access to vital resources, such as health care

Proteomic Detection of Non-Annotated Protein-Coding Genes in Pseudomonas fluorescens Pf0-1

Genome sequences are annotated by computational prediction of coding sequences, followed by similarity searches such as BLAST, which provide a layer of possible functional information. While the existence of processes such as alternative splicing complicates matters for eukaryote genomes, the view of bacterial genomes as a linear series of closely spaced genes leads to the assumption that computational annotations that predict such arrangements completely describe the coding capacity of bacterial genomes. We undertook a proteomic study to identify proteins expressed by Pseudomonas fluorescens Pf0-1 from genes that were not predicted during the genome annotation. Mapping peptides to the Pf0-1 genome sequence identified sixteen non-annotated protein-coding regions, of which nine were antisense to predicted genes, six were intergenic, and one read in the same direction as an annotated gene but in a different frame. The expression of all but one of the newly discovered genes was verified by RT-PCR. Few clues as to the function of the new genes were gleaned from informatic analyses, but potential orthologs in other Pseudomonas genomes were identified for eight of the new genes. The 16 newly identified genes improve the quality of the Pf0-1 genome annotation, and the detection of antisense protein-coding genes indicates the under-appreciated complexity of bacterial genome organization