Human Resources/hrm assignment
1."Empirical research in India creates so many problems for researchers".state the problems that are usually faced by young researcher.
2."interpretation is an art of drawing inference depending upon the skill of researcher".elucidate the given statement with various techniques of interpretation.
Question: 1."Empirical research in India creates so many problems for researchers".state the problems that are usually faced by young researcher.
Problems Faced By Researcher
What is a Research Problem? A research problem is an issue or concern that an investigator presents and justifies in a research study. A problem that someone would like to research
Problems Faced by RESEARCHER
• Time and Money :- Time and Money is one of the biggest problem that a researcher faced during the research.• We know that Time is Money if we loose our time than we are loosing the money. During a Research Researchers have to waste their time for collecting the information from the various sources.
• Lack of Computerization :- There is a problem related to Computerization which researchers generally face. A researcher cant find the data for the research because of lack of computerization. People generally used to record the data in the various books.• So this is also one of the problem that is faced by researcher.
• Confidence :- Most of the business units in our country do not have the confidence that the material supplied by them to researchers will not be misused and as such they are often reluctant in supplying the needed information to researchers.
• Library Management :- Library management and functioning is not satisfactory at many places and much of the time and energy of researchers are spent in tracing out the books, journals, reports, etc., rather than in tracing out relevant material from them.
• Distance :- There is insufficient interaction between the university research departments on one side and business establishments, government departments and research institutions on the other side. A great deal of primary data of non-confidential nature remain untouched or untreated by the researchers for want of proper contacts.
Other Problems Faced by Researcher 1. The lack of a scientific training in the methodology of research. 2. This causes unnecessary delays in the completion of research studies. 3. All possible efforts be made in this direction so that efficient secretarial assistance is made available to researchers and that too well in time.
4. There is also the difficulty of timely availability of published data from various government and other agencies doing this job in our country.5. There is also the problem that many of our libraries are not able to get copies of old and new Acts/Rules, reports and other government publications in time.
Challenges facing young researchers
1. • Capacity Building for Research: aYoung Researcher Perspective.
2. A gain, proposalwriting? Where do I start? I will quit
3. Thinking: Young Researcher’sDilemmaThe human brain tries its hardest to simplify lifeby setting up routine patterns of perceptionand action.Once you identify the pattern you flow along itwithout further effort.For example, if you had to think through whichorder to put on 6 pieces of clothing you wouldnever leave the house as you need to sortthrough 47,000 combinations, but we getdressed quickly each day.
4. The problem we have is that in youngresearchers, this mechanism kicks in all toofrequently.That is, when they are faced with a problem /opportunity they implicitly rely on thesepatterns of perception (which have developedfrom a combination of their skills, experienceand knowledge) to solve it – this is NOTthinkingThinking is the discipline of restraining thenatural impulses and forcing the brain to dealwith what is in front of it, not what it ‘perceives’is in front of
5. What makes thinking difficult?• “Birds fly, when they get tired they land. Man thinks,when he gets tired he says ‘I understand’” - case of students in class and exam– Thinking is made more difficult by the confusion oftwo forms of complexity:
Detail complexity – the type of complexity that occursdue to the number of variables- based around ‘how’ tobring the detail together and create order
Dynamic complexity - The type of complexity thatoccurs due to the nature of the interrelationshipsbetween variables- based around ‘identifying’ thevariables and ‘understanding’ the interrelationshipsbetween them.
6. Is young researcher lazy?Natural for most youngresearchers– Left to Right-Start at thebeginning and work ourway through to the end(this is easier because youalways move into an area you already know)– Outcomes are whatever wee nd up with– Bottom Up-Primarilyconcerned with how to pullinformation together– People naturally start at alevel of detail they arecomfortable with, but notnecessarily related to theproblem / opportunity– Limits the development ofinsightNatural for few youngresearchers– Right to Left-Start at theend (outcomes) and workback to the beginningidentifying how to get there(this is more difficultbecause you have to ‘think’about where you are going)– Outcomes are planned andrarely adjusted– Top Down-Primarilyconcerned with the developof insight (interrelationshipbetween variables)– Requires starting at thelevel of problem /opportunity and workingdown
7. What is a proposal: Does itrequire thinking?• It is a plan or a scheme that persuades itsreaders/panel to accept the idea written by onefirm/researcher as a response to a request fromanother firm and can be also written without anyprior request. It aims at obtaining commercialcontracts.• It is a written offer to undertake a project fordesigning, creating something new or forchanging or modifying an existing procedure,method, system or structure within a specifiedperiod of time.
8. Types of proposals• External proposals : A proposal written by a firmin order to win contracts for work.• Internal proposals : The writer prepares aninternal proposal with a motive to convince theperson or a group in authority to allow him toimplement his ideas. It is submitted within acompany.
9. • Solicited proposals : It is a proposal preparedin response to an invitation from a firm or somegovernment or non- government organization.These invitations are published in the newspapers as tender notices. The writer is requiredto supply relevant particulars, as demanded bythe firm to win the project.• Unsolicited proposals : It is a proposalprepared by an individual on his own initiative,without any external encouragement or request ,to solve a problem or to meet a specific need asperceived by him and convince the authority toallow the writer to implement his idea.
10. • Have you ever written aproposal?
11. Characteristics of a proposal- Can theyoung researcher understand?• Since it is persuasive in nature it should bebased on the AIDA plan• Attention• Passive attention-the involuntary process directed by externalevents that stand out from their environment, such as a bright flash,a strong odor, or a sudden loud noise.• Active attention-multidimensional cognitive process that includes theability to select and focus on what is important at any given moment,the ability to consistently maintain mental effort while performingtasks that require mental energy and the ability to inhibit action orthought while previewing alternative actions or thoughts• Interest is created by pointing out how the workwill be executed. Do you have the expertise?
12. • Desire has to be generated to accept theproposal by highlighting the benefits andadvantages- can you foresee outputs?• Action is induced by persuasivereasoning- Can you stand up and gatheryour tools?
13. Common Mistakes by young researchersin Proposal Writing:• Failure to provide the proper context to framethe research question.• Failure to delimit the boundary conditions for theresearch.• Failure to cite landmark studies-limitedknowledge• Failure to accurately present the theoretical andempirical contributions by other researchers.• Failure to stay focused on the research question.
14. • Failure to develop a coherent and persuasiveargument for the proposed research.• Too much detail on minor issues, but notenough detail on major issues.• Too much rambling going “all over the map”without a clear sense of direction.• Too many citation lapses and incorrectreferences.•
15. Why are young researcher’s proposalsturned down?• The problem is trivial or is unlikely to produce new oruseful information- total lack of originality.• The proposed research is based on a hypothesis thatrests on doubtful, unsound or insufficient evidence- theresearcher does not have evidence• The problem is more complex than the researcherrealizes- case of water hyacinth• Intellectual immaturity• The problem as proposed is overly involved with toomany elements required to be investigatedsimultaneously- Lack of focus• The description of the research leaves the proposaldiffuse and without a clear aim.
16. Critical challenges living withyoung researcher!!!!!• Plagiarism- Case of German Minister ofEd.-• submitting someone else’s text as one’sown or attempting to blur the line betweenone’s own ideas or words and thoseborrowed from another source, and• carelessly or inadequately citing ideas andwords borrowed from another source.• Question: why is plagiarism rampant?
17. • Model Roles- compare research models tosports, entertainment, politics………..• Social and economic-• Work environment- library resources,teaching load, office environment………• Institutional policies- what is JOOUSTpolicy regarding research?
18. • Addiction to social networks• Quality of training and supervision-statistical methods, problem identification(other components of proposal)• Identity with funding agencies-are youknown??
19. Way Forward• Research policy• Measurement of research and extensionproportion by individual researchers• Established researchers to pull up theirsocks• Research groups/networks establishedand monitored•
2."interpretation is an art of drawing inference depending upon the skill of researcher".elucidate the given statement with various techniques of interpretation.
Interpretation: Analyzing What a Text Means
This final level of reading infers an overall meaning. We examine features running throughout the text to see how the discussion shapes our perception of reality. We examine what a text does to convey meaning: how patterns of content and language shape the portrayal of the topic and how relationships between those patterns convey underlying meaning.
Repeating v. Analyzing: Making The Leap
Rightly or wrongly, much of any student's career is spent reading and restating texts. For many, the shift to description and interpretation is particularly hard. They are reluctant to trade the safety of repeating an author's remarks for responsibility fortheir ownassertions. They will freely infer the purpose of an action, the essence of a behavior, or the intent of a political decision. But they will hesitate to go beyond what they take a text to "say" on its own. They are afraid to take responsibility for their own understanding. Others are so attuned to accepting the written word that they fail to see the text as a viable topic of conversation.
Look at Leonardo da Vinci's painting Mona Lisa, and you see a woman smiling. But you are also aware of a painting. You see different color paint (well, not in this illustration!) and you see how the paint was applied to the wood. You recognize how aspects of the painting are highlighted by their placement or by the lighting.
When examining a painting, you are aware that you are examining a work created by someone. You are aware of an intention behind the work, an attempt to portray something a particular way. Since the painting does not come out and actively state a meaning, you are consciously aware of your own efforts to find meaning in the painting: Is she smiling? Self-conscious? Alluring? Aloof?
Looking at the Mona Lisa, you know that you are not looking at Mona Lisa, a person, but The Mona Lisa, a painting. You can talk not only about the meaning of the picture, but also about how it was crafted. What is the significance of the dream landscape in the background? Why, when we focus on the left side of the picture, does the woman looks somehow taller or more erect than if we focus on the right side? The more features of the painting that you recognize, the more powerful your interpretation will be.
When reading texts, as when reading paintings, we increase understanding by recognizing the craftsmanship of the creation, the choices that the artist/author made to portray the topic a certain way. And yet there is still that feeling that texts are somehow different. Texts do differ from art insofar as they actually seem to come out and say something. There are assertions "in black and white" to fall back on. We can restate a text; we cannot restate a painting or action. Yet a text is simply symbols on a page. Readers bring to their reading recognition of those symbols, an understanding of what the words mean within the given social and historical context, and an understanding of the remarks within their own framework of what might make sense, or what they might imagine an author to have intended.
There is no escape; one way or another we are responsible for the meaning we find in our reading. When a text says that someone burned their textbooks, that is all that is there: an assertion that someone burned their textbooks. We can agree on how to interpret sentence structure enough to agree on what is stated in a literal sense. But any sense that that person committed an irresponsible, impulsive, or inspired act is in our own heads. It is not stated as such on the page (unless the author says so!). Stories present actions; readers infer personalities, motives, and intents. When we go beyond the words, we are reading meaning.
Readers infer as much, if not more, than they are told. Readers go beyond the literal meaning of the words to find significance and unstated meanings—and authors rely on their readers' ability to do so! The reader's eye may scan the page, but the reader's mind ranges up, down, and sideways, piecing together evidence to make sense of the presentation as a whole.
A number of observations should be made lest there be misunderstanding.
All Three Modes of Reading and Discussion Are Legitimate
The models are designed to identify varying levels of sophistication and insight in reading and discussion. While one approach may be more complex than another, no one way of reading a text is necessarily better than another. They are simply different, and involve different observations and reasoning. The key thing is to know which style of reading you want to do at any time, how to do it, and how to tell whether you are actually doing it successfully.
All Reading Involves More Than One Form of Reading
The divisions between the three modes of reading are, to some extent, artificial. Dividing reading into reading what a text says, does, and means is somewhat like dividing bicycle riding into concern for balance, speed, and direction. They are all necessary and affect one another. Speed and direction both affect balance; we will fall off, or crash, without all three. And yet we may focus on one or another at any particular time. We can parse each out for analysis.
While the modes of reading and discussing texts can be separated out for purposes of discussion, and it is relatively easy to distinguish between the resulting forms of discussion, in practice these reading techniques overlap. Any particular text can, and will, be read at various levels of understanding at once. We cannot understand what a text says without recognizing relationships between sentences. We cannot even understand sentences without drawing inferences that extend beyond the words on the page. Observations and realizations at any one level of reading invariably support and spark observations at another. Observations characteristic of all three forms of response can be included in an interpretation.
Finally, while it is relatively easy to distinguish between forms of discussion.—restatement, description, and interpretation—a description might include restatement for the purposes of illustration, and an interpretation may be supported with descriptions of various portions of the text and even restatement of key points (see the example above). In the end, the "highest" level of remark characterizes the discussion a whole.
These Are Not the Only Ways To Respond To a Text
Restatement, description and interpretation are not the only ways one can respond to a text. But they are the ones of interest here, if only because they are the responses that must precede most other forms of response. Readers can obviously offer their own ideas on a topic—but that does not fall under the topic of discussing a text. Readers can criticize an author's handling of a topic based on their own knowledge or views, evaluate the writing style, or attack the honesty of the author. These too are legitimate forms of response, but they require a critical reading of the text first if they are to be meaningful. The first order of business is to make sense of the text, and it is with that task that our efforts are concerned here.
Finally, we might note that book reports or reviews often contain additional elements, such as a feeling for the writing style, comparison to other works, the reviewer's emotional response to the reading experience, or the circumstances of publication. And book reviewers often use the book under reviews as a taking-off point for a discussion of the topic itself—all elements that go beyond, but depend on, a careful reading of the text in question. •
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true.[ The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.
Alternatively, inference may be defined as the non-logical, but rational means, through observation of patterns of facts, to indirectly see new meanings and contexts for understanding. Of particular use to this application of inference are anomalies and symbols. Inference, in this sense, does not draw conclusions but opens new paths for inquiry.
In this definition of inference, there are two types of inference: inductive inference and deductive inference. Unlike the definition of inference in the first paragraph above, meaning of word meanings are not tested but meaningful relationships are articulated.
Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference.
Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. This generalizes deterministic reasoning, with the absence of uncertainty as a special case. Statistical inference uses quantitative or qualitative (categorical) data which may be subject to random variation.
Greek philosophers defined a number of syllogisms, correct three part inferences, that can be used as building blocks for more complex reasoning. We begin with a famous example:
1. All men are mortal
2. Socrates is a man
3. Therefore, Socrates is mortal.
The reader can check that the premises and conclusion are true, but Logic is concerned with inference: does the truth of the conclusion follow from that of the premises?
The validity of an inference depends on the form of the inference. That is, the word "valid" does not refer to the truth of the premises or the conclusion, but rather to the form of the inference. An inference can be valid even if the parts are false, and can be invalid even if some parts are true. But a valid form with true premises will always have a true conclusion.
For example, consider the form of the following symbological track:
1. All meat comes from animals.
2. Beef is a type of meat.
3. Therefore, beef comes from an animal.
If the premises are true, then the conclusion is necessarily true, too.
Now we turn to an invalid form.
1. All A are B.
2. C is a B.
3. Therefore, C is an A.
To show that this form is invalid, we demonstrate how it can lead from true premises to a false conclusion.
1. All apples are fruit. (Correct)
2. Bananas are fruit. (Correct)
3. Therefore, bananas are apples. (Wrong)
A valid argument with false premises may lead to a false conclusion:
1. All tall people are Greek.
2. John Lennon was tall.
3. Therefore, John Lennon was Greek. (wrong)
When a valid argument is used to derive a false conclusion from false premises, the inference is valid because it follows the form of a correct inference.
A valid argument can also be used to derive a true conclusion from false premises:
1. All tall people are musicians (although wrong)
2. John Lennon was tall (right, Valid)
3. Therefore, John Lennon was a musician (Right)
In this case we have two false premises that imply a true conclusion.
Example for definition #2[
Evidence: It is the early 1950s and you are an American stationed in the Soviet Union. You read in the Moscow newspaper that a soccer team from a small city in Siberia starts winning game after game. The team even defeats the Moscow team. Inference: The small city in Siberia is not a small city anymore. The Soviets are working on their own nuclear or high-value secret weapons program.
Knowns: The Soviet Union is a command economy: people and material are told where to go and what to do. The small city was remote and historically had never distinguished itself; its soccer season was typically short because of the weather.
Explanation: In a command economy, people and material are moved where they are needed. Large cities might field good teams due to the greater availability of high quality players; and teams that can practice longer (weather, facilities) can reasonably be expected to be better. In addition, you put your best and brightest in places where they can do the most good—such as on high-value weapons programs. It is an anomaly for a small city to field such a good team. The anomaly (i.e. the soccer scores and great soccer team) indirectly described a condition by which the observer inferred a new meaningful pattern—that the small city was no longer small. Why would you put a large city of your best and brightest in the middle of nowhere? To hide them, of course.
An incorrect inference is known as a fallacy. Philosophers who study informal logic have compiled large lists of them, and cognitive psychologists have documented many biases in human reasoning that favor incorrect reasoning..
Automatic logical inference[
AI systems first provided automated logical inference and these were once extremely popular research topics, leading to industrial applications under the form of expert systemsand later business rule engines. More recent work on automated theorem proving has had a stronger basis in formal logic.
An inference system's job is to extend a knowledge base automatically. The knowledge base (KB) is a set of propositions that represent what the system knows about the world. Several techniques can be used by that system to extend KB by means of valid inferences. An additional requirement is that the conclusions the system arrives at are relevant to its task.
Example using Prolog[
Prolog (for "Programming in Logic") is a programming language based on a subset of predicate calculus. Its main job is to check whether a certain proposition can be inferred from a KB (knowledge base) using an algorithm called backward chaining.
Let us return to our Socrates syllogism. We enter into our Knowledge Base the following piece of code:
mortal(X) :- man(X).
( Here :- can be read as "if". Generally, if P → Q (if P then Q) then in Prolog we would code Q:-P (Q if P).)
This states that all men are mortal and that Socrates is a man. Now we can ask the Prolog system about Socrates:
(where ?- signifies a query: Can mortal(socrates). be deduced from the KB using the rules) gives the answer "Yes".
On the other hand, asking the Prolog system the following:
gives the answer "No".
This is because Prolog does not know anything about Plato, and hence defaults to any property about Plato being false (the so-called closed world assumption). Finally ?- mortal(X) (Is anything mortal) would result in "Yes" (and in some implementations: "Yes": X=socrates)
Prolog can be used for vastly more complicated inference tasks. See the corresponding article for further examples.
Use with the semantic web[
Recently automatic reasoners found in semantic web a new field of application. Being based upon description logic, knowledge expressed using one variant of OWL can be logically processed, i.e., inferences can be made upon it.
Bayesian statistics and probability logic[
Philosophers and scientists who follow the Bayesian framework for inference use the mathematical rules of probability to find this best explanation. The Bayesian view has a number of desirable features—one of them is that it embeds deductive (certain) logic as a subset (this prompts some writers to call Bayesian probability "probability logic", following E. T. Jaynes).
Bayesians identify probabilities with degrees of beliefs, with certainly true propositions having probability 1, and certainly false propositions having probability 0. To say that "it's going to rain tomorrow" has a 0.9 probability is to say that you consider the possibility of rain tomorrow as extremely likely.
Through the rules of probability, the probability of a conclusion and of alternatives can be calculated. The best explanation is most often identified with the most probable (seeBayesian decision theory). A central rule of Bayesian inference is Bayes' theorem.
A relation of inference is monotonic if the addition of premises does not undermine previously reached conclusions; otherwise the relation is nonmonotonic. Deductive inference is monotonic: if a conclusion is reached on the basis of a certain set of premises, then that conclusion still holds if more premises are added.
By contrast, everyday reasoning is mostly nonmonotonic because it involves risk: we jump to conclusions from deductively insufficient premises. We know when it is worth or even necessary (e.g. in medical diagnosis) to take the risk. Yet we are also aware that such inference is defeasible—that new information may undermine old conclusions. Various kinds of defeasible but remarkably successful inference have traditionally captured the attention of philosophers (theories of induction, Peirce's theory of abduction, inference to the best explanation, etc.). More recently logicians have begun to approach the phenomenon from a formal point of view. The result is a large body of theories at the interface of philosophy, logic and artificial intelligence.
• Abductive reasoning
• Deductive reasoning
• Inductive reasoning
• Retroductive reasoning
• Reasoning System
• Bayesian inference
• Frequentist inference
• Business rule
• Business rules engine
• Expert system
• Fuzzy logic
• Immediate inference
• Inference engine
• Inferential programming
• Logic of information
• Logical assertion
• Logical graph
• Nonmonotonic logic
• Rule of inference
• List of rules of inference
• Transduction (machine learning)
• Sherlock Holmes