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Everything You Need to Know About Conflicts of Interest (Part I) – Psychology Today (Sara Gorman & Jack M. Gorman | January 2017)0

Posted by Admin in on March 18, 2017
 

Is transparency the only solution?

In September of 2016, a shocking expose in The New York Times revealed that everything we thought we knew about sugar, fat, and heart disease was wrong. And not only was it wrong, but the information we had been using to guide our decisions about what to eat and what to feed our kids had been manipulated in what can only be described as a conspiracy between scientists and the sugar industry.

Needless to say, people were outraged. As one reader of The New York Times article commented, “This was a conspiracy of scientific FRAUD. The sugar companies that did this should be sued for $BILLIONS for the health harm that they caused.” It wasn’t long before comparisons to the tobacco industry started: “Sugar is the new tobacco and has been for a while. The article is just the tip of the iceberg,” commented another NYT reader.

And then, in the midst of election season, came the conspiracy theories: “FYI.. Hillary very well funded by Big Sugar so you can bet nothing will happen as a result of these findings. With Hillary in the White House, we’ll all be eating cake anyway- It’s a win win for everyone!”

Read the rest of this discussion piece
This is Part I of this series
Go to Part II of this series
Go to Part III of this series*

* Part III doesn’t really discuss Conflicts of interest in research of any CoI so though we link to it here we’ve not included Part III in the Resource Library

2017 UK Parliamentary Office of Science and Technology POSTNOTE 544 January 2017 Integrity in Research0

Posted by Admin in on March 17, 2017
 

A  POSTnote that considers current approaches to promoting integrity in research.

Integrity in research refers to the behaviours and values that result in high quality, ethical and valuable research. This POSTnote considers current approaches to fostering an environment conducive to good research in the UK, and detecting and preventing practices that fall short of expected standards. It also examines the current mechanisms for supporting integrity in the UK, whether these are sufficient, or if another form of oversight, such as regulation, might be preferable.

Poor practice ranges from minor errors to serious misconduct. While deliberate fraud does occur, it is thought to be extremely rare. Questionable research practices are a more widespread concern, as they are thought to be more prevalent and have a greater impact on the research record.

There are concerns about how to maintain integrity in research, because of fears that the ‘publish or perish’ culture leads to poor or questionable research practices. While many mechanisms do exist for reducing poor practice, and these are thought to have a positive effect on reducing such behaviour, there remain concerns that the system is disjointed, lacks openness and transparency, and that the incentive structure is such that good practice is not recognised or rewarded. Strategies for tackling this therefore focused on reducing institutional pressures on researchers, through enhancing openness and transparency, improving oversight and training, and re-aligning incentives for researchers so that they are rewarded for engaging in rigorous and accurate research.

Read the rest of this discussion paper

Safeguarding research integrity in China (Papers: Jane Qiu | 2015)0

Posted by Admin in on March 11, 2017
 

China has an impressive record in the total number of scientific publications in the past decade. In 2012, it churned out 193 733 Science Index Citation papers—4.7 times the 2002 level and second only to the United States.

Unfortunately, the standards of science integrity has not kept up with the pace of this development, and many cases of research misconduct have been reported. This prompts many to fear that the country is now facing a critical problem in the field of scientific ethics.

In a forum chaired by National Science Review’s executive associate editor Mu-ming Poo, five panellists from diverse backgrounds discuss how serious the problem is, what the root causes are, and how to safeguard research integrity in China.

Jane Qiu (2015) Safeguarding research integrity in China. National Science Review (March 2015) 2 (1): 122-125. doi: 10.1093/nsr/nwv002
Publisher (open access): https://nsr.oxfordjournals.org/content/2/1/122.full.pdf+html

Predictive Analytics in Higher Education: Five Guiding Practices for Ethical Use (Resources | March 2017)0

Posted by Admin in on March 9, 2017
 

Five Guiding Practices for Ethical Use

Contents
….Introduction
….Guiding Practice 1: Have a Vision and Plan
….Guiding Practice 2: Build a Supportive Infrastructure
….Guiding Practice 3: Work to Ensure Proper Use of Data
….Guiding Practice 4: Design Predictive Analytics Models and Algorithms that Avoid Bias
….Guiding Practice 5: Meet Institutional Goals and Improve Student Outcomes by Intervening with Care

Introduction
Colleges are under increasing pressure to retain their students. Federal and state officials are demanding that those who enter their public institutions— especially students from underrepresented groups— earn a degree. Over two dozen states disburse some state funding on how many students an institution graduates, rather than how many it enrolls. Students and families are more anxious than ever before about crossing the degree finish line, as the financial burden of paying for college has increased significantly in recent years. And retaining students is becoming more crucial to the university bottom line. As recruiting and educating students becomes increasingly expensive, colleges hope to balance the resources they use to recruit students with revenue generated when those students are retained.

Because of these pressures, institutions have begun analyzing demographic and performance data to predict whether a student will enroll at an institution, stay on track in her courses, or require support so that she does not fall behind. Using data in this way is known as predictive analytics. Analyzing past student data to predict what current and prospective students might do has helped institutions meet their annual enrollment and revenue goals with more targeted recruiting and more strategic use of institutional aid. Predictive analytics has also allowed colleges to better tailor their advising services and personalize learning in order to improve student outcomes

Manuela Ekowo and Iris Palmer (2017) Predictive Analytics in Higher Education: Five Guiding Practices for Ethical Use. New America. https://www.newamerica.org/education-policy/policy-papers/predictive-analytics-higher-education/

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