A dilemma of language: ‘‘Natural disasters’’ in academic literature

For decades sections of the academic community have been emphasizing that disasters are not natural. Nevertheless, politicians, the media, various international organizations—and, more surprisingly, many established researchers working in disaster studies—are still widely using the expression ‘‘natural disaster.’’ We systematically analyzed the usage of the expression ‘‘natural disaster’’ by disaster studies researchers in 589 articles in six key aca- demic journals representative of disaster studies research, and found that authors are using the expression in three principal ways: (1) delineating natural and human-induced hazards; (2) using the expression to leverage popularity; and (3) critiquing the expression ‘‘natural disaster.’’ We also identified vulnerability themes that illustrate the con- text of ‘‘natural disaster’’ usage. The implications of con- tinuing to use this expression, while explicitly researching human vulnerability, are wide-ranging, and we explore what this means for us and our peers. This study particu- larly aims to stimulate debate within the disaster studies research community and related fields as to whether the term ‘‘natural disaster’’ is really fit for purpose moving forward

“Critique is not a verb”:is peer review stifling the dialogue in disaster scholarship?

Purpose: In this position piece, we will reflect on some of our recent experiences with the peer review process in disaster studies and show how debate can so easily be stifled. We write it as a plea for healthy academic argumentative discussion and intellectual dialogue that would help all of us to refine our ideas, respect others’ ideas and learn from each other.Approach: We provide reflection on our own experiences. All the examples here are based on the anonymous (double-blinded) peer reviews that we have received in the past 2 years in response to papers submitted to disaster-related journals.Findings: We show that the grounds for rejection often have nothing to do with the rigour of the research but are instead based on someone’s philosophy, beliefs, values or opinions that differ from that of the authors, and which undermine the peer-review process.Originality: There is so much potential in amicable and productive disagreements, which mean that we can talk together – and through this, we can learn. Yet the debate in its purest academic sense is a rare beast in disaster scholarship – largely because opposing views do not get published. We call for is that ideological judgement and self-interest are put aside alongside our pride when peers’ work is reviewed – and that intellectual critique is used in a productive way that would enhance rather than stifle scholarship.</div

Die Goldapotheke

DIE GOLDAPOTHEKE Die Goldapotheke / Meding, Oskar (Public Domain) ( - ) Erster Band ( - ) Title page ( - ) I. ( - ) II. ([40]) III. ([76]) IV. ([109]) V. ([137]) VI. ([183]) VII. ([210]) VIII. ([234]) IX. ([259]) Advertising ( - ) Zweiter Band ( - ) Title page ( - ) X. ( - ) XI. ([30]) XII. ([55]) XIII. ([81]) XIV. ([102]) XV. ([131]) XVI. ([169]) XVII. ([211]) XVIII. ([225]) XIX. ([259]) XX. ([272]) Advertising ( - ) ColorChart ( -

Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns

Background: Continuous Integration (CI) is an agile software development practice that involves producing several clean builds of the software per day. The creation of these builds involve running excessive executions of automated tests, which is hampered by high hardware cost and reduced development velocity. Goal: The goal of our research is to develop a method that reduces the number of executed test cases at each CI cycle.Method: We adopt a design research approach with an infrastructure provider company to develop a method that exploits Ma-chine Learning (ML) to predict test case verdicts for committed sourcecode. We train five different ML models on two data sets and evaluate their performance using two simple retrieval measures: precision and recall. Results: While the results from training the ML models on the first data-set of test executions revealed low performance, the curated data-set for training showed an improvement on performance with respect to precision and recall. Conclusion: Our results indicate that the method is applicable when training the ML model on churns of small size

Drivers of Applying Ecological Modernization to Construction Waste Minimization in New South Wales Construction Industry

The application of ecological modernization (EM) (to delink industry growth from environmental damage) to minimize construction waste has not been explored within the construction industry in general, and the New South Wales (NSW) construction industry in particular. This study seeks to identify the drivers of applying EM to construction waste minimisation (CWM) in the industry. Also, to determine the CWM measures that are critical for each of the drivers. A survey was adopted in this study to target stakeholders engaged in the delivery of construction projects in NSW from design to completion. The survey was selected to reach a large number of respondents within a manageable period. A pilot study was conducted to ensure the reliability of the research design before a full-scale data collection was launched. The data from 240 valid responses was analysed using factor analysis, relative importance index and descriptive statistics. The results revealed five important drivers for EM’s application to CWM. These are agents of change, government policies, supply chain dynamics, skill-building and technological innovations. The CWM measures that are critical for each of these drivers were also identified. The study provides insights into the application of EM to address the construction industry problem of waste generation as by-product of its growth. It also shows the ability to protect the environment while enabling continuous economic growth. Furthermore, it demonstrates the applicability of EM to minimize the construction waste of NSW construction industry