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How to read in a statistic whatever you want

Professor at chalkboardIt’s a common place that you can read nearly any result you want from a statistic. You just have to optimize your mathematical model or cut short the reasoning about the data. There is currently a JAMA publication from the American Medical Society which is cited in many magazines and newspapers (even in German Spiegel) as “22% less risk of colorectal cancer for vegetarians”. No ordinary reader of these reviews will have a look at the original numbers in the publication since it is not freely available (yet another reason for open Publication …). But here they are:

  • Vegetarian participants: 40367
    Cancer cases: 252
  • Nonvegetarian participants: 37292
    Cancer cases: 238

This makes for the following relative case numbers:

  • Vegetarian: 0,624 / 100 participants
  • Nonvegetarian: 0,638 / 100 participants

Or a difference of 0,014 cases per 100 people. This means, if you eat meat your risk to come down with a form of colorectal cancer increases by 0,014 percent. This reads quite different, doesn’t it?

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Changing filenames to camel case

Sometimes you’ll encounter the task to rename files named with snake_case to CamelCase. And sometimes there are a lot of those files. For example when porting a CakePHP 1 project to CakePHP 2 or 3. In CakePHP the upgrade console does a decent job renaming a lot of files for you. But in larger projects having subfolders you’re left with an awful lot of unrenamed scripts. This is where my Python 3 script comes in. It renames any filenames in the current directory given as parameters (thanks to Python 3 argparse you can use wildcards!) from snake_case to CamelCase filename. It preserves the extension if the filename has one. It also has a quiet mode (use -q) to suppress any output at the command line and a preview mode just like GNU make (use -n, supersedes -q), which doesn’t do anything but print out what it would have done.

(Picture by Startup Stock Photos CC 0,

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Knowledge series, Part 1 – Human capital vs. Human resources

2631466945_de1bbc2cfd_zThe term human capital was coined by american economist Theodore Schultz in his seminal paper “Investment in Human Capital” from 1961 [1]. He starts by discussing that often the consideration of human beings as a form of capital is seen as unethical. To count human beings as capital is justified by a rationale drawing on comparisons and discussions from the beginning of the 20th century, comparing e.g. this approach to sacrificing a hundred human beings in order to save a gun in war. Well, our views on war have changed since 1906. This might explain why today we tend to abhor counting humans as capital.

But there is a second approach originating in the theories of french sociologist Pierre Bourdieu readily summarized in 1986 [2]. He derives his concept of cultural capital from observations of french upper class children gaining substantial advantages in their life compared to working class children resulting from their education. He states that their parents invest economic capital (money) in the education of their children. They accumulate a certain amount of cultural capital which can later on be converted back to economic capital when the owners of the cultural capital get more profitable jobs because of their better education. This cultural capital is saved in what he calls the embodied form which means that the knowledge and education belongs naturally to the person acquiring it. It can not be sold like a physical item. When investing e.g. in a collection of paintings this capital can be seen as transformed into another form of economic capital since the paintings have a certain economic value. But the consumption and appreciation of art and its display at social events also counts as a form of cultural capital, this time in objectified state. When acquiring an academic title the title itself represents the embodied cultural capital of the title holder in a certain institutionalized state.

In contrast the term human resources is nowadays widely used to describe all that concerns staffing or personnel management. It was first used by american economist John Commons in 1893 [3] and was commonplace (please excuse the pun) in economic literature from the 1910s on. It represents the view of employees as assets to the firm. This interpretation is opposed by the preamble of the United Nations International Labour Organization including the principle “Labour is not a commodity“.

When forced to  choose one of the two terms I would opt for “human capital” since its philosophical and sociological implications put the human being at least a bit more in the focus of interest.


[1] Schultz, T. W.. (1961). Investment in Human Capital. The American Economic Review, 51(1), 1–17

[2] The Forms of Capital: English version published 1986 in J.G. Richardson’s Handbook for Theory and Research for the Sociology of Education, pp. 241–258.

[3] John R. Commons, The Distribution of Wealth, New York, 1893

(Picture by Ian Muttoo CC BY-ND 2.0,

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Knowledge in and out

wp-1456109997332.jpgIn my last post I mused about employer policies concerning training and learning of employees. I would like to use this as an introduction to a series of postings related to knowledge management and innovation in firms. Much has been written and discussed about the creation and finding of knowledge. From a practical point of view and in philosophy, which discusses these topics as epistemology. In this first installment I would like to give an overview of what’s ahead and how I would like to structure my texts.

My first approach was to put all of this in two or three postings. Since this is way too much text to be read comfortably I will divide the topics in a more or less natural way like this:

  1. The idea of human capital in contrast to human resources
  2. Philosophy and theory of knowledge, from information via knowledge to innovation
  3. Flow of knowledge in the firm and exchange with external resources
  4. Knowledge management from a social network point of view
  5. Challenges for human resources departments concerning knowledge management
  6. Impediments for knowledge creation and usage (e.g. Not-Invented-Here-Syndrome, NIH)
  7. The state of open innovation
  8. Simulation of knowledge flow and NIH using cellular automata

What do you think? Interested? Is something missing? Should I reorder the topics? Let me know!

(Picture by Tommy Ellis CC BY-ND 2.0,