Cultivating Talent through a Principal Pipeline

This report, the second in a series, describes early results of Wallace's Principal Pipeline Initiative, a multi-year effort to improve school leadership in six urban school districts. The report describes changes in the six districts' practices to recruit, train and support new principals. It also offers early lessons for other districts considering changes to their own principal pipelines

Strategies for Improving the Diversity of the Health Professions

Evaluates programs and strategies that were designed to increase the number of underrepresented African Americans, Native Americans, and Latinos in the health professions in California. Includes recommendations

Districts Taking Charge of the Principal Pipeline

Six urban school districts received support from The Wallace Foundation to address the critical challenge of supplying schools with effective principals. The experiences of these districts may point the way to steps other districts might take toward this same goal. Since 2011, the districts have participated in the Principal Pipeline Initiative, which set forth a comprehensive strategy for strengthening school leadership in four interrelated domains of district policy and practice:Leader standards to which sites align job descriptions, preparation, selection, evaluation, and support.Preservice preparation that includes selective admissions to high-quality programs.Selective hiring, and placement based on a match between the candidate and the school.On-the-job evaluation and support addressing the capacity to improve teaching and learning, with support focused on needs identified by evaluation.The initiative also brought the expectation that district policies and practices related to school leaders would build the district's capacity to advance its educational priorities. The evaluation of the Principal Pipeline Initiative has a dual purpose: to analyze the processes of implementing the required components in the participating districts from 2011 through 2015; and then to assess the results achieved in schools led by principals whose experiences in standards-based preparation, hiring, evaluation, and support have been consistent with the initiative's requirements. This report addresses implementation of all components of the initiative as of 2014, viewing implementation in the context of districts' aims, constraints, and capacity

Ontology Population for Open-Source Intelligence

We present an approach based on GATE (General Architecture for Text Engineering) for the automatic population of ontologies from text documents. We describe some experimental results, which are encouraging in terms of extracted correct instances of the ontology. We then focus on a phase of our pipeline and discuss a variant thereof, which aims at reducing the manual effort needed to generate pre-defined dictionaries used in document annotation. Our additional experiments show promising results also in this case

Ontology population for open-source intelligence: A GATE-based solution

Open-Source INTelligence is intelligence based on publicly available sources such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but because of the vast amount of documents available, automatic mechanisms for their population are needed, starting from the crawled text. This paper presents an approach for the automatic population of predefined ontologies with data extracted from text and discusses the design and realization of a pipeline based on the General Architecture for Text Engineering system, which is interesting for both researchers and practitioners in the field. Some experimental results that are encouraging in terms of extracted correct instances of the ontology are also reported. Furthermore, the paper also describes an alternative approach and provides additional experiments for one of the phases of our pipeline, which requires the use of predefined dictionaries for relevant entities. Through such a variant, the manual workload required in this phase was reduced, still obtaining promising results

HIP to COLLEGE 2012-2015: Creating Strong Funding and Nonprofit Networks for Latino Student Success

For the past three-and-a-half years, through the HIP to College initiative, Hispanicsin Philanthropy has worked diligently to strengthen the academic success of Latino students and the long-term community advancement that results from their earning postsecondary degrees. With the support of generous partners, such as the Bill & Melinda Gates Foundation, the HIP to College initiative in North Carolina and Colorado worked to develop networks that support Latino students through high school and into college. Improving outcomes for Latino students is the priority of the HIP Education Focused Initiative. The success of this initiative has been remarkable. HIP is optimistic about the future of this work and its role in cultivating an educational landscape and partnerships in the United States that help Latino students thrive

Mining Frequency of Drug Side Effects Over a Large Twitter Dataset Using Apache Spark

Despite clinical trials by pharmaceutical companies as well as current FDA reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. In this work, we have improved on previous pipelines in sentimental analysis-based mining, processing, and extracting tweets with drug-caused side effects. We have also added a new ensemble classifier using a combination of sentiment analysis features to increase the accuracy of identifying drug-caused side effects. In addition, the frequency count for the side effects is also provided. Furthermore, we have also implemented the same pipeline in Apache Spark to improve the speed of processing of tweets by 2.5 times, as well as to support the process of large tweet datasets. As the frequency count of drug side effects opens a wide door for further analysis, we present a preliminary study on this issue, including the side effects of simultaneously using two drugs, and the potential danger of using less-common combination of drugs. We believe the pipeline design and the results present in this work would have great implication on studying drug side effects and on big data analysis in general

Iowa Department of Commerce, Iowa Utilities Board Division Performance Report, FY 2004

Agency Performance Repor

Iowa Department of Commerce, Iowa Utilities Board Division Performance Report, FY 2008

Agency Performance Repor