Assignment Code: MLIE-105/AST/TMA/Jul.2022-Jan.2023

Course Code: MLIE-105

Assignment Name: Informetrics and Scientometrics

Year: 2022-2023

Verification Status: Verified by Professor

1.1 Define the concept of growth of knowledge. Briefly describe the different models of growth of scientific knowledge and publications in scientific specialties.

Ans) The study of how scientific communities affect the growth of knowledge shows how important social interaction is to the growth of science. If this were true, it would be interesting to see what models of intellectual change fit with these findings. And then, whether these also fit into the pattern of more publications and new authors in research areas. Different ways to study how knowledge grows are talked about, and models that are often used for this kind of study are briefly described. Then, the criteria for choosing a model are laid out, and finally, the life cycle of a research specialty is explained. There are many ways to study the growth of knowledge, such as the sociological approach, the epidemiological approach, the self-organizing systems approach, and the biological evolution approach. However, we will only talk about the sociological approach, which looks at how the number of publications and authors in a scientific field shows the growth of knowledge.

Models of growth of scientific knowledge and publications in scientific specialties

The fact that scientific knowledge and publications in scientific specialties grow in a way that looks like a logistic growth curve suggests that scientists' social relationships change as science grows. This makes me wonder what the connection is between how society is changing and how scientific fields are getting smarter. Are they on their own? Three growth models have been used to describe the different stages of scientific knowledge growth in scientific specialties:

First Model: In this model, the growth of knowledge is seen as a steady accumulation of new ideas that build on ideas that came before them. When a theory-based hypothesis is put up against real-world evidence, it is either accepted or rejected.

Second Model: In this model, it is assumed that new ideas don't come from the most recent discoveries, but rather from any discovery in the history of a scientific field. In this model, things from the whole history of the cultural area are chosen by chance.

Third Model: In this model, the growth of knowledge is seen as a steady increase that is broken up by periods of change.

2.1 Explain different methods of graphical representation of data giving suitable examples. (10)

Ans) Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

1. Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.

2. Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.

3. Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.

4. Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.

5. Frequency Table – The table shows the number of pieces of data that falls within the given interval.

6. Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.

7. Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.

8. Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

3.1What is informativeness‟? Discuss the seven properties of informativeness. (10)

Ans) The concept or view of relevance that L. Schamber, M. B. Eisenberg, and M. S. Nellan presented is similar to the idea or view of informativeness. According to them, relevance is a changing idea that depends on how users judge the quality of the link between the information in an information item and the information need they have at that moment.

Seven properties of informativeness

Property 1: Informativeness is a number that can't be negative and is related to how records interact with users. Different people may find different things in a record or text to be helpful.

Property 2: One can't directly measure how informative something is. But the user's reference for ranking records based on how much information they have keeps the order of informativeness values. So, a user might be asked to help make a list of how informative different texts, records, or documents are.

Property 3: Informativeness doesn't always work the same way or add up when it's put together with other things. When records or texts are "catalogued," they are read or used one after the other (i.e., one after another). The ranking of how informative a set of records is may be affected by the order in which they are put together (the sequence for interaction or use).

Property 4: The total amount of information in a record or document stays the same no matter how it is broken up. Partitioning means separating into separate parts. This property says that the sum of the usefulness of the parts of the record is the same as the usefulness of the whole record (or document). But the order of the partitions needs to be kept. If the order of the partitions is broken or changed, this property is no longer true. The word for this is subsequence. The breaking up or granularization can be done at the sentence level.

Property 5: When records are ordered by non-increasing user reference, i.e., the more informative record is given a lower rank than the less informative record; if there are ties, the ranks can be chosen at random, the informativeness of a subsequence is roughly proportional to the logarithm of the number of records in the subsequence.

Property 6: The usefulness of an information service's answer to a user's question depends on how complete and in what order the records looked at during the retrieval process are compared to those in an ideal chain of records. This chain is made up of the records that can fully meet a user's information needs.

Property 7: The informativeness of an information service organisation for a user community (real or potential) is how well the service meets the community's information needs. It depends on how well the organisation meets the needs of the community and how timely and informative each service is.

4.1 Differentiate between science indicators and R& D statistics. Discuss the objectives of S&T indicators for developing countries.

Ans) Difference between science indicators and R& D statistics as follows:

The National Council of Research in Italy's Giorgio Sirilli, "Science indicators are used to track changes in the scientific enterprise and its parts over time. This lets us see where strengths and weaknesses are starting to show up. When these indicators are updated regularly, they can give early warnings of trends and, when looked at as a whole, can help decision-makers understand how the many variables that describe the work of a scientific system work together. So, they can help those who decide what the business's priorities are and how much money it gets ".

"Indicators aren't just statistics, and statistics aren't automatically indicators, unless a theory or assumption ties the indicator variable to something that it doesn't directly measure."

Gerald Holton, a physicist at Harvard University, says that the word "indicator" should only be used for a measure that explicitly tests an assumption, hypothesis, or theory. For data, the assumptions, hypotheses, or theories that are being tested remain hidden. Indicators are a more complex result of how theory and measurement interact with each other. An analogy is the best way to show what the difference is between data and indicators. The measurement of a patient's blood pressure that is dropping quickly stays as data until it is seen by someone who knows enough about physiology to know that it is a sign of a change in the patient's health.

Objectives of S&T Indicators for the Developing Countries

The indicators that need to be made should fit the local social system, whether it's the national economy or the science and technology system. Because of the different ways similar types of activities are set up in different places, statistics that are the same do not always show the same or similar things. Indicators of science and technology that work well in the United States and the European Union may be misleading or not useful in Sub-Saharan Africa. Indicator development must be linked to research on the specific features of the formal and informal science and technology (S&T) systems in the types of societies in question.

In developing countries, the best case scenario would be for S&T indicators to:

1. "Give some ideas about how S&T efforts affect the country's social and economic growth.

2. Help countries make inventories so they can figure out how good their S&T is.

3. Find out how much a country's science and technology system is part of the world system or depends on it.

4. Based on how well they did in science and technology in the past, help set priorities between different fields, institutions, or research groups that are competing for limited funds.

5. Help research institutes run their research projects better.

5.0 Write short notes on any two of the following: (10)

a) Types of scales

Ans) There are four different scales of measurement.

Nominal Scale: A nominal scale is the 1st level of measurement scale in which the numbers serve as “tags” or “labels” to classify or identify the objects. A nominal scale usually deals with the non-numeric variables or the numbers that do not have any value.

Ordinal Scale: The ordinal scale is the 2nd level of measurement that reports the ordering and ranking of data without establishing the degree of variation between them. Ordinal represents the “order.” Ordinal data is known as qualitative data or categorical data. It can be grouped, named and also ranked.

Interval Scale: The interval scale is the 3rd level of measurement scale. It is defined as a quantitative measurement scale in which the difference between the two variables is meaningful. In other words, the variables are measured in an exact manner, not as in a relative way in which the presence of zero is arbitrary.

Ratio Scale: The ratio scale is the 4th level of measurement scale, which is quantitative. It is a type of variable measurement scale. It allows researchers to compare the differences or intervals. The ratio scale has a unique feature. It possesses the character of the origin or zero point

b) User Studies

Ans) User studies are mostly about the people who use the product. When we look at the results of these studies, we can see how often people use different kinds of documents and services. In use studies, we care mostly about how the documents and services are used, not about the people who use them. For example, when we do citation studies, we find out how often different kinds of documents are used, but we don't know much about the kinds of people who use those documents.

The purpose of user studies are:

1. To figure out the different kinds of people who use a library or information centre.

2. To figure out the kinds of people who use a service or product that gives information.

3. To figure out what kinds of documents are used a lot, a little, or not at all, and why.

4. To figure out what information services and products aren't being used at all and why.

5. To find out how happy the users are with the site.

6. To find out what's good and bad about different collections.

7. To base the policy on how to grow the collection on facts.

8. To add to the collection in a way that is based on science.