There is nothing new about the Knowledge Café or is there? by David Gurteen

https://www.linkedin.com/pulse/20141208122315-343667-there-is-nothing-new-about-the-knowledge-caf%C3%A9-or-is-there

There is nothing new about the Knowledge Café or is there? 

When people say that something is not new, they usually mean that they are familiar with the concept and its in common practice. 

To my mind, when this objection is levelled at the Knowledge Cafe - it means that they do not fully understand it. 

When I look at how organizations operate and the behaviours of people in organizations - it is quite apparent that people are either not aware of the fundamental principles and the power of good conversation or they understand them but do not to change their way of doing things either out of habit, laziness or choice. 

Why in meetings and presentations are we still so dependent on Powerpoint? Why is the dominant format of a talk, a long presentation with lots of Powerpoint slides and a very short time for Q&A? Why is no time included for reflection and no time for conversations amongst the participants in order for them to engage with the topic or issue? Why do we insist on talking at each other rather than with each other. 

Why is the dominant layout of our meeting rooms: either lecture style or large tables, when we know from experience and observation that these layouts are not conducive to good conversation? The research shows that good conversations take place in small groups of 3 or 4 people sitting around a small round table or even no table at all. 

Why in meetings, especially those where the people do not know each other well, do we not allow time for socialisation and relationship building before getting down to business when again the research shows that such socialisation improves people's cognitive skills. Why are circles rarely used in meeting's when the research and our own personal experience demonstrates their power? 

Why do managers and facilitators seek to control meetings so tightly and are afraid of negative talk or dissent. By surpressing people's fears, doubts and uncertainties - you do not eliminate them - you just drive them underground. Peter Block says "Yes" has no meaning if there is not the option to say "No". You need to bring people's doubts and fears out into the open and talk about them at length. 

And why when we know from research that group intelligence relates to how members of a team talk to each other. That it depends on the social sensitivity of the group members and on the readiness of the group to allow members to take equal turns in the conversation. And that groups where one person dominates are less collectively intelligent than in groups where the conversational turns are more evenly distributed, do we allow the same old people to dominate the conversations in our meetings and do nothing to encourage the quieter ones to engage and speak up. 

The Knowledge Cafe may not be totally new but it addresses all these issues and more but as a conversational method is still sadly very poorly adopted. 

In fact in many organizations conversation is seen as wasting time. But slowly this is changing. More and more people are starting to understand the power of conversation and take a conversational approach to the way that they connect, relate and work with each other. They see themselves as Conversational Leaders.

The Most Hilarious Proofreading Mistake in a Scientific Paper Ever by George Dvorsky

http://io9.com/the-most-hilarious-proofreading-mistake-in-a-scientific-1657839235

This is an actual quote from a scientific paper, published recently — and apparently without editing. Apparently the authors didn't think much of one of the papers they were citing. And their publisher didn't bother to edit out their pre-publication snark.

Ugh, this is not the kind of thing you want to see in a scientific journal. It makes us lose faith in peer review, and by consequence, the scientific method itself.

Four months after being published, someone finally noticed that a fish mating paper in the journal Ethology — "Variation in Melanism and Female Preference in Proximate but Ecologically Distinct Environments" — contained a rather embarrassing passage that both the authors and the peer reviewers failed to notice.

As Retraction Watch reported yesterday, the journal quickly removed the paper after the issue was brought to light.

Later, corresponding author Zach Culumber told Retraction Watch:

No, this was not intentional. It was added into the paper by a co-author during revision (after peer-review). It was unfortunately an oversight that became incorporated into the paper during the process of sending the manuscript back and forth between co-authors. The comment in question was not spotted during the proofing process with the journal. Neither myself nor any of the co-authors have any ill-will towards any other investigators, and I would never condone this sentiment towards another person or their work. We are working with the Journal now to correct the mistake. As the corresponding author, I apologize for the error.

Wiley says it's going to investigate the error and republish a corrected version as soon as possible, which now appears to have been done.

Does Media Violence Predict Societal Violence? It Depends on What You Look at and When by Christopher J. Ferguson

http://onlinelibrary.wiley.com/doi/10.1111/jcom.12129/pdf

ABSTRACT:
This article presents 2 studies of the association of media violence rates with societal violence rates. In the first study, movie violence and homicide rates are examined across the 20th century and into the 21st (1920–2005). Throughout the mid-20th century small-to-moderate correlational relationships can be observed between movie violence and homicide rates in the United States. This trend reversed in the early and latter 20th century, with movie violence rates inversely related to homicide rates. In the second study, videogame violence consumption is examined against youth violence rates in the previous 2 decades. Videogame consumption is associated with a decline in youth violence rates. Results suggest that societal consumption of media violence is not predictive of increased societal violence rates.

Popular Mechanics: 6 Warning Signs That a Scientific Study is Bogus

http://www.popularmechanics.com/science/health/6-warning-signs-that-a-scientific-study-is-bogus-16674141

Was the Paper Published in a Peer-Reviewed Journal?

"If it wasn't, you have no reason to trust it," says Ivan Oransky, former executive editor at Reuters and cofounder of the blog Retraction Watch. "The peer-review system, as flawed as it is, stands between us and really poor science." Also, find out if the journal or its publisher is on Jeffrey Beall's list of questionable open-access journals, at scholarlyoa.com. 

What is the Journal's Impact Factor?

The impact factor is the average number of times a journal's papers are cited by other researchers. You can usually find this information on the journal's home page or by searching "impact factor" along with its name. Check out the impact factor of other journals in that field of research to see how they compare. 

Do the Researchers Cite Their Own Papers?

If so, this is a red flag that they are promoting views that fall outside the scientific consensus. Citations are listed at the end of a paper. 

How Many Test Subjects Were Used?

A large number of test subjects makes a study more robust and reduces the likelihood that the results are random. In general, the more questions a paper asks, the greater its sample size should be. Most reliable papers contain something called a p-value, which measures the probability (p) that a study's results occurred by random chance. In science a p-value of 0.05 suggests the study's conclusions may be meaningful. Smaller p-values are better. 

Does it Rely on Correlation?

Cigarette smoking has declined dramatically in the U.S. in the past few decades, and so has the national homicide rate. But just because two events occur at the same time doesn't mean that one caused the other. 

Have the Results Been Reproduced?

To find out, search the paper's name on Google Scholar and click on the Cited By link beneath the name. This will list other researchers who mention the paper in their own publications, and may also give you a clearer view of how other researchers critiqued the paper. 

Jane Goodall On The Importance Of Empathy In Science

Surveys Can Make People Go Extreme by Esther Inglis-Arkell

http://io9.com/surveys-can-make-people-go-extreme-1621840701

There are all kinds of reasons why people don't tell the truth when asked questions. Sometimes they suddenly turn into fanatics. They hate, or love, anything. Here's how you catch people when they go extreme, or when they try to just get along.

We already know that people deliberately lie when given surveys on sex and drugs, but they also lie when given surveys about the importance of flossing and whether people should smoke in shopping malls. The difference is, many people don't even know that they're lying. People are driven to exaggerate (or even invent) their likes and dislikes, and so when they're asked to score, from one to five, their support for an issue or agreement with a statement, they avoid the middle and go right for one and for five.

This bias, called "extreme response bias" has annoyed many manufacturers, or politicians, who believed their targeted audience was passionately in favor of a new flavor of coke or a ban on littering, trotted the idea out, and gotten a lackluster response. Sometimes people are actually passionate about a subject, and sometimes they just want to be that way. Researchers took a look at separating out the two. They came up with a few guidelines to tell if people were inflating their opinions.

First of all, the more options you give a person, the more likely they are to go for the fringe opinion. A survey asking people to rate their experience on a scale of one to five will get far fewer extreme responses than a survey that asks people to rate their experience on a scale from one to ten. Individually, people with more education tend to be less extreme in their responses. The most telling variable, though, is another kind of bias.

Acquiescence bias is the tendency of a surveyed individual to go along with whatever the surveyor suggests. This is why researchers agonize over trying to make each question as neutral as possible. Ask people "don't you think smoking should be completely banned in malls," and they will tend to say yes. Ask them, "don't you think people should be allowed to smoke in public malls," and they will also tend to say yes. In order to be accurate, researchers can't tip their hands and let people know what answer they expect, or want. If, on the other hand, what the researchers want is to tell how many people responding to their questions are just going along with it, they can put out two surveys, one with a question that tips people one way, and one with a re-worded version of the question that tips people the other way.

Acquiescence bias tends to be a harbinger of extreme response. If people aren't going to be honest - either with the surveyors or themselves - they're at least going to be enthusiastically dishonest. So the more acquiescence everyone gets, the more extremity they should expect to see.

New algorithm identifies data subsets that will yield the most reliable predictions by Larry Hardesty

http://phys.org/news/2014-07-algorithm-subsets-yield-reliable.html#jCp

Much artificial-intelligence research addresses the problem of making predictions based on large data sets. An obvious example is the recommendation engines at retail sites like Amazon and Netflix.

But some types of data are harder to collect than online click histories —information about geological formations thousands of feet underground, for instance. And in other applications—such as trying to predict the path of a storm—there may just not be enough time to crunch all the available data.

Dan Levine, an MIT graduate student in aeronautics and astronautics, and his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics, have developed a new technique that could help with both problems. For a range of common applications in which data is either difficult to collect or too time-consuming to process, the technique can identify the subset of data items that will yield the most reliable predictions. So geologists trying to assess the extent of underground petroleum deposits, or meteorologists trying to forecast the weather, can make do with just a few, targeted measurements, saving time and money.

Levine and How, who presented their work at the Uncertainty in Artificial Intelligence conference this week, consider the special case in which something about the relationships between data items is known in advance. Weather prediction provides an intuitive example: Measurements of temperature, pressure, and wind velocity at one location tend to be good indicators of measurements at adjacent locations, or of measurements at the same location a short time later, but the correlation grows weaker the farther out you move either geographically or chronologically.