difference between mode 1 and mode 2
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Mode 2 is a concept that is often used to refer to a novel way of scientific knowledge production, (or rather its "co-production"), put forth in 1994 by Michael Gibbons, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott and Martin Trow in their book The new production of knowledge: the dynamics of science and research in contemporary societies(Sage). It is also the nickname of a graffiti artist born in Mauritius 1967.
Gibbons and colleagues argued that a new form of knowledge production started emerging from the mid 20th century which is context-driven, problem-focused and interdisciplinary. It involves multidisciplinary teams brought together for short periods of time to work on specific problems in the real world. Gibbons and his colleagues labelled this "mode 2" knowledge production. This he and his colleagues distinguished from traditional research, which they labelled "mode 1", which is academic, investigator-initiated and discipline-based knowledge production. So mode 1 knowledge production is investigator-initiated and discipline-based while mode 2 is problem-focused and interdisciplinary. Or as Limoges (1996:14-15) wrote -
- We now speak of 'context-driven' research, meaning 'research carried out in a context of application, arising from the very work of problem solving and not governed by the paradigms of traditional disciplines of knowledge.
John Ziman drew a similar distinction between academic science and post-academic science in 2000 in his book Real Science (Cambridge).
In 2001 Helga Nowotny, Peter Scott and Michael Gibbons published Re-thinking science: knowledge in an age of uncertainty (Polity) in which they extend their analysis to the implications of mode 2 knowledge production for society.
While the notion of mode 2 knowledge production has attracted considerable interest, it has not been universally accepted in the terms put by Gibbons and colleagues. Scholars in science (policy) studies have pointed to three types of problems with the concept of Mode 2, regarding its empirical validity, its conceptual strength and its political value (Hessels and Van Lente, 2008).
Concerning the empirical validity of the Mode 2 claims, Etzkowitz & Leydesdorff (2000:116) argue â€“
- The so-called Mode 2 is not new; it is the original format of science (or art) before its academic institutionalization in the 19th century. Another question to be answered is why Mode 1 has arisen after Mode 2: the original organizational and institutional basis of science, consisting of networks and invisible colleges. Where have these ideas, of the scientist as the isolated individual and of science separated from the interests of society, come from? Mode 2 represents the material base of science, how it actually operates. Mode 1 is a construct, built upon that base in order to justify autonomy for science, especially in an earlier era when it was still a fragile institution and needed all the help it could get (references omitted).
In the same article Etzkowitz & Leydesdorff (2000:111) use the notion of the triple helix of the nation state, academia and industry to explain innovation, the development of new technology and knowledge transfer. Etzkowitz & Leydesdorff (2000:118) argue that â€˜The Triple Helix overlay provides a model at the level of social structure for the explanation of Mode 2 as an historically emerging structure for the production of scientific knowledge, and its relation to Mode 1â€™.
Steve Fuller, in his book The Governance of Science (Chapter 5) has criticised the 'Modists' view of the history of science because they wrongly give the impression that mode 1 dates back to seventeenth-century Scientific Revolution whereas mode 2 is traced to the end of either World War II or the cold war, whereas in fact the two modes were institutionalized only within a generation of each other (the third and the fourth quarters of the nineteenth century, respectively). Fuller claims that the Kaiser Wilhelm Institutes in Germany, jointly funded by the state, the industry and the universities, predated today's "triple helix" institutions by an entire century.
Regarding the conceptual strength of Mode 2, it has been argued that the coherence of its five features is questionable. There might be a lot of multi-disciplinary, application oriented research that does not show organizational diversity or novel types of quality control (Rip, 2002).
Another problem with Mode 2 is that it lends itself to a normative reading. Several authors have criticized the way Gibbons and his co-authors seem to blend descriptive and normative elements. According to Godin (1998), the Mode 2 talk is more a political ideology than a descriptive theory. Similarly, Shinn (2002:604) complains: 'Instead of theory or data, the New Production of Knowledge - both book and concept - seems tinged with political commitment'.
Some writers have invented a mode 3 knowledge, which is mostly used to refer to emotional knowledge or social knowledge. But these writers miss the whole point of Gibbons et al. which was not to catalogue types of knowledge but to describe types of knowledge production or research.
In statistics, the mode is the value that occurs most frequently in a data set or a probability distribution. In some fields, notably education, sample data are often called scores, and the sample mode is known as the modal score.
Like the statistical mean and the median, the mode is a way of capturing important information about a random variable or a population in a single quantity. The mode is in general different from the mean and median, and may be very different for strongly skewed distributions.
The mode is not necessarily unique, since the same maximum frequency may be attained at different values. The most ambiguous case occurs in uniform distributions, wherein all values are equally likely.
Mode of a probability distribution
As noted above, the mode is not necessarily unique, since the probability mass function or probability density function may achieve its maximum value at several points x1, x2, etc.
The above definition tells us that only global maxima are modes. Slightly confusingly, when a probability density function has multiple local maxima it is common to refer to all of the local maxima as modes of the distribution. Such a continuous distribution is called multimodal (as opposed to unimodal).
In symmetric unimodal distributions, such as the normal (or Gaussian) distribution (the distribution whose density function, when graphed, gives the famous "bell curve"), the mean (if defined), median and mode all coincide. For samples, if it is known that they are drawn from a symmetric distribution, the sample mean can be used as an estimate of the population mode.
Mode of a sample
The mode of a data sample is the element that occurs most often in the collection. For example, the mode of the sample [1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17] is 6. Given the list of data [1, 1, 2, 4, 4] the mode is not unique - the dataset may be said to be bimodal, while a set with more than two modes may be described as multimodal.
For a sample from a continuous distribution, such as [0.935..., 1.211..., 2.430..., 3.668..., 3.874...], the concept is unusable in its raw form, since each value will occur precisely once. The usual practice is to discretize the data by assigning frequency values to intervals of equal distance, as for making a histogram, effectively replacing the values by the midpoints of the intervals they are assigned to. The mode is then the value where the histogram reaches its peak. For small or middle-sized samples the outcome of this procedure is sensitive to the choice of interval width if chosen too narrow or too wide; typically one should have a sizable fraction of the data concentrated in a relatively small number of intervals (5 to 10), while the fraction of the data falling outside these intervals is also sizable. An alternate approach is kernel density estimation, which essentially blurs point samples to produce a continuous estimate of the probability density function which can provide an estimate of the mode.
The following MATLAB code example computes the mode of a sample:
X = sort(x); indices = find(diff([X; realmax]) > 0); % indices where repeated values change [modeL,i] = max (diff([0; indices])); % longest persistence length of repeated values mode = X(indices(i));
The algorithm requires as a first step to sort the sample in ascending order. It then computes the discrete derivative of the sorted list, and finds the indices where this derivative is positive. Next it computes the discrete derivative of this set of indices, locating the maximum of this derivative of indices, and finally evaluates the sorted sample at the point where that maximum occurs, which corresponds to the last member of the stretch of repeated values.
Comparison of mean, median and mode
When do these measures make sense?
Unlike mean and median, the concept of mode also makes sense for "nominal data" (i.e., not consisting of numerical values). For example, taking a sample of Korean family names, one might find that "Kim" occurs more often than any other name. Then "Kim" would be the mode of the sample. In any voting system where a plurality determines victory, a single modal value determines the victor, while a multi-modal outcome would require some tie-breaking procedure to take place.
Unlike median, the concept of mean makes sense for any random variable assuming values from a vector space, including the real numbers (a one-dimensional vector space) and the integers (which can be considered embedded in the reals). For example, a distribution of points in the plane will typically have a mean and a mode, but the concept of median does not apply. The median makes sense when there is a linear order on the possible values. Generalizations of the concept of median to higher-dimensi
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Answers:seems obvious #1 consumes more energy. #1 runs for 5 hours. #2 runs for 3 hours (I assume on-off-on-off-on which adds up to 5 hours) Or is number 2 running for a total of 5 hours on (on-off-on-off-on-off-on), in which case they are about equal with startup surges adding a bit of power to case #2. OR are you not telling us that the AC is actually cycling on/off in response to the thermostat in each case? But you did say continuous operation. You can tell you will get a better answer if you tell us all the information we need! PS the same result would apply for a TV
Answers:Are you sure of the data? Range is the difference between the maximum and the minimum. Mode is the value of the peak in the distribution. Median is that value which divides the population into half above and half below the median. Mean is the arithmetical average of the data distribution. With the median, mode and the range being the same, how the mean is different? If possible, pl. send the raw data separately to me after emailing me.
Answers:So you have the mean of 4.846. For the median, arrange them, either in the ascending or descending order, and find the middle: Since there are 52 films in all, 52/2 = 26. So, the middle is the 26th film. in the ascending order: [0,3] == 1st to 21st film [3,6] == 22nd to 38th (21+17) film So, 26 is between 3 and 6. The gap from 3 to 6 has a range of 3, which includes 17 films. Thus every film occupies a range of 3/17: 22nd film = 3 to 3+3/17 23rd film = 3+3/17 to 3+6/17 24th film = 3+6/17 to 3+9/17 25th film = 3+9/17 to 3+12/17 26th film = 3+12/17 to 3+15/17 The midpoint of 3+12/17 and 3+15/17 is 3+13.5/17 = 0.794118 The mode is the midpoint of the interval with the largest frequency: [0,3] 21 So the mode is (0+3)/2 = 1.5 So the correct answer would be b).
Answers:I thought I already answered a very similar question or is it the same person posting again? I feel there is some mistake in the data as already pointed out. With range, median and mode being same, how the mean alone is changing? Unless the raw data is also presented, it is difficult to make sense.