Some of you may have heard that Wikipedia is not suitable for serious research, for many reasons. That's true. Here is the article where they officially agree with this fact. And here are some replies to the most common objections perceived, such as it being prone to e-vandalism, and being written mostly by non-experts.
Despite this fact, I personally have for quite some time considered it a valuable source of pointers for further reading. Its articles often provide succint introductions to common or modern terms, facts or people, making it also a useful (IMHO) definitions dictionary (besides Google's 'define:' label, of course). Also, in my experience, most articles have valid links to useful reading, supporting at least most of the article claims.
So, one might think it would be enough to make it reputable to assign qualified editors to each section or area (by some definition of qualification), and have them moderate edits, while still accepting general public contributions. Guess what, our wait might have ended. As per this hint from Science I just found out, some people (formerly?) tied to wikipedia created a fork project
Bookmark this:
http://citizendium.org/
And, yes, citizendium has an article on wikipedia and vice versa, just like Uncyclopedia has one. Not as funny, though.
2007-06-22
The System (of how not to waste time when studying)
In trying to organize my reading ToDo list, I developed a SYSTEM in which I put my faith upon, that will quickly give me the means to learn the important things I must, in order to pass the first step of the doctoral routine.
It consists in classifying topics or articles or discussions into one of four categories, namely:
1- EFFICIENT
2- USEFUL
3- LOGICAL
4- INTERESTING
The above-mentioned categories are rated in decreasing order of importance. Now some explaining is due. In the following, I will refer to (topic|article|discussion|conversation) simply as "topic".
EFFICIENT means the topic will make be better in doing something I can already do, or avoid doing something in an inefficient, or unprofessional way. A simple example is when you learn to program your first algorithms in scientific computing (say, in Matlab/Octave for starters), the first thing you learn (or at least it should be) after it works, is to vectorize the code. Following this simple example, the EFFICIENT kind of stuff is of prime importance, hence it's number 1 priority in the list.
USEFUL is similar. The topic in question is important to learn, but maybe not ASAP. It will probably be used in a somewhat near future, so it receives number 2 priority.
LOGICAL is as in Spock's usual saying. I may not need the topic itself, but it broadens the understanding of an EFFICIENT or USEFUL topic, so it just makes sense to seek some degree of deep understanding in the later, by reading the LOGICAL topics.
INTERESTING is something I would really like to read or learn, but has whatsoever no positive impact in the above mentioned framework. It must be dealt with in the spare time. Actually I created this mostly to accommodate music listening and geek stuff I like.
If a topic doesn't fit in any of the above categories, it must be discarded at once.
(I can imagine Dr. Spock saying: "This is illogical").
There. The system. The method.
If a more complex framework is needed, one can adapt it to use weights into each category. Maybe some Linear Programming can be done in order to decide how much time exactly one should spend on each topic in one's list. This would certainly be interesting. Unfortunately, for me, not logical, so...
It consists in classifying topics or articles or discussions into one of four categories, namely:
1- EFFICIENT
2- USEFUL
3- LOGICAL
4- INTERESTING
The above-mentioned categories are rated in decreasing order of importance. Now some explaining is due. In the following, I will refer to (topic|article|discussion|conversation) simply as "topic".
EFFICIENT means the topic will make be better in doing something I can already do, or avoid doing something in an inefficient, or unprofessional way. A simple example is when you learn to program your first algorithms in scientific computing (say, in Matlab/Octave for starters), the first thing you learn (or at least it should be) after it works, is to vectorize the code. Following this simple example, the EFFICIENT kind of stuff is of prime importance, hence it's number 1 priority in the list.
USEFUL is similar. The topic in question is important to learn, but maybe not ASAP. It will probably be used in a somewhat near future, so it receives number 2 priority.
LOGICAL is as in Spock's usual saying. I may not need the topic itself, but it broadens the understanding of an EFFICIENT or USEFUL topic, so it just makes sense to seek some degree of deep understanding in the later, by reading the LOGICAL topics.
INTERESTING is something I would really like to read or learn, but has whatsoever no positive impact in the above mentioned framework. It must be dealt with in the spare time. Actually I created this mostly to accommodate music listening and geek stuff I like.
If a topic doesn't fit in any of the above categories, it must be discarded at once.
(I can imagine Dr. Spock saying: "This is illogical").
There. The system. The method.
If a more complex framework is needed, one can adapt it to use weights into each category. Maybe some Linear Programming can be done in order to decide how much time exactly one should spend on each topic in one's list. This would certainly be interesting. Unfortunately, for me, not logical, so...
Greetings
This is going to be the space I'll be using to keep record of interesting academic articles and related news.
Areas I am mainly interested at present include Mathematics, Statistics, Genomics and Evolution & Selection. Actually, in the reversed order.
I often allow myself to divert to farther topics such as Astronomy & Cosmology, Physics and Palenteology, which are both intriguing and misterious, given my almost absolute ignorance in the field. These and other fields might appear ocasionally, but I intend my focus to be related to my current work, whatever that may be.
Feel free to share thoughts in the comments area. I am always willing to learn.
But please, if you cite facts, be kind enough to include some (valid|authoritary) sources.
Areas I am mainly interested at present include Mathematics, Statistics, Genomics and Evolution & Selection. Actually, in the reversed order.
I often allow myself to divert to farther topics such as Astronomy & Cosmology, Physics and Palenteology, which are both intriguing and misterious, given my almost absolute ignorance in the field. These and other fields might appear ocasionally, but I intend my focus to be related to my current work, whatever that may be.
Feel free to share thoughts in the comments area. I am always willing to learn.
But please, if you cite facts, be kind enough to include some (valid|authoritary) sources.
Subscribe to:
Posts (Atom)