International Organizations - Science topic

Thank you very much for the information. This is just what I needed

The concepts of "Green Market Paradigm Shift Knowledge Gaps" and "Dwarf Green Market Paradigm Flip Knowledge Gaps" represent critical areas of concern in the context of environmental sustainability and market dynamics. These knowledge gaps signify the disparities in our understanding of the evolving green economy and the challenges it presents.

The "Green Market Paradigm Shift Knowledge Gaps" pertain to the lack of comprehensive insights into the transformative changes occurring in the global economy as it increasingly shifts towards sustainability, eco-friendliness, and renewable resources. Addressing these gaps is crucial for policymakers, businesses, and researchers to harness the potential of this green transition effectively.

On the other hand, "Dwarf Green Market Paradigm Flip Knowledge Gaps" refer to the often overlooked or underestimated aspects within this shift, which could have significant impacts if ignored. They signify a potential blind spot in academia and policymaking, which, if not recognized, might hinder progress toward a truly sustainable global economy.

Both these knowledge gaps underscore the importance of robust research and comprehensive understanding in shaping a more sustainable and environmentally responsible future, ensuring that we do not inadvertently hinder our progress by neglecting critical aspects of the green market paradigm shift.

Interesting and important research initiative, especially for Europe.

Wishing you further success.

Haji Saediman thank you very much for your opinion!

fake

sorry for posting 3 times. i was accidentally .

Thank you Dr Shapira for your kind response. I deeply value your insight from your works in the field. I will buy the book authored by you -"Mismanagement, "Jumpers"and morality :Covertly " from Amazon. It is certainly insightful as you have shown from your work at Kibbutz in Israel.

Our so-called experts don't know when to say "No, I don't know ". This arrogance of power is one of the bane in effective partnership .

Dear Olivier,

Your questions are central to my recent research. The evidence from our work in Central in Eastern Africa shows that the contribution of MSPs to SDGs are moderated through different factors and strategies on harnessing MSPs depends on these factors. Some of the factors are strategic funding for events and key organizations in the networks, capacity of intervention team (champion, facilitator, organizer and monitor) and the current stage of the MSPs. There are some detailed information on the factors in the links below.

To understand which specific MSP factors are relevant where, I developed a data management and decision support system that informs the intervention managers on the contribution of MSPs to SDGs called Learning System for Agricultural Research for Development (LESARD). Here are some information about LESARD and the evidence it generated. 

Dear colleague

Here in Cuba is used as a vaccine against classical swine fever Chinese strain, it is considered that this strain is replicated only in tonsils and for no more than 30 days, the question is What vaccine strain did you use to immunize?

No vaccine eliminates the virus, only the severe forms of the disease are controlled; however, the low virulence strains may be refractory to the vaccine and cause chronic or inapparent forms of the disease, in which case the lesions are totally non-specific.

When low-virulence strains are circulating, it may be the case that sows bear piglets that never recognize the virus as an antigen, so they do not develop an immune response, these pigs usually develop the disease after weaning or die from secondary infections.

Therefore if the forms it has are chronic or inapparent it is better to use immunohistochemical techniques, which do differentiate the vaccine virus from the field virus.

For arthritis problems I suggest you to investigate mycoplasmas, streptococci or porcine erysipelas

I hope you find the information useful

Attached herein.

Hi Laís,

There are a few things I can recommend as texts dealing with Foucault's notions of power and knowledge production. Here are a couple:

Faubion, J.  (Ed.). (2000), Michele Foucault: Power. New York, NY: The New Press.

Foucault, M. (1972). The archaeology of knowledge and the discourse on language. New York, NY: Vintage Books.

Gordon, C. (Ed.) (1980). Power/Knowledge: Selected interviews and other writings 1972-1977 by Michele Foucault. New York, NY: Pantheon Books.

I would also suggest that there is implicit mention of power and knowledge in Foucault's work on subject creation, particularly in the following:

Foucault, M. (1977). Discipline & punish: The birth of the prison. New York, NY: Vintage Books.

Foucault, M. (1978). The history of sexuality, Volume I. New York, NY: Vintage Books.

These are just a couple off the top of my head, but there are certainly others that may be of use. Let me know if I can be of further assistance!

Cheers,

Elizabeth

 Dear madam

Assessment of learning has multi-dimensional uses. Besides assessing performance of students in learning , it can throw light on the efficacy of instructional objectives, suitability of curriculum, effectiveness of  methods of transaction in and out of classroom and teaching learning materials such as textbooks,audio video ,lab kits.  It  can give feedback even about the assessment techniques.  In addition to end of term assessment , it  is essential that it is integrated with teaching -learning process .

Thanks and regards

R S SINDHU

Sarika Bansal 's report  is a valuable one. We must learn from failure and be open to speak out about our failure. The FAILFaire is also an interesting idea. This kind of media is necessary to be open to failure. In this regards, I have to thank Ritu Verma for making me aware of Sarika Bansal 's report.

Each project has its own failure and we should detect it. However, as Ritu Verma suggests, there are so many failures and we must question if there are some structural problems. In this regards, we must question the current state of three fields:

Development economics started 1940's and 50's. At that time, structuralists were prevalent. Although they were too much inclined to central or government planning, they were right in stressing the lack of social and economic conditions that a market economy works well and normally. With the arrival of neoclassical revolution in development economics, the economists who work on development started to think with macro data and reasoned that the market failure is a result of regulations. They recommended market-friendly policies. I agree that there are many unnecessary regulations but we must also see that some of them are crucial to the well-being of the people and the latter is the very basis of economic development.  We should question the state of the art of development economics.

Development studies seems to have started with some discontent vis-à-vis development economics dominated by neoclassical  economists. Development studies include varied fields. I am interested Discourse Analysis but this is not our subject now. Although they are not uniquely responsible, development studies remained rather separated from development economics. This indifference towards economics implied (with no intention) the indifference towards examining conditions for economic development.

International trade theory is important, because no country or area can be separated from the world economy, if you like it or not. Many of successful Asian economies used foreign trade strategically. There are wide spread understanding that these economies adopted free trade policy. Except the case of Singapore and  Hong Kong, this is a kind of urban legend and false. We must learn more closely the secrets of successful countries such as Korea, Taiwan, Thailand, Malaysia and others. Why could they get a (relative) success in their economic building. With this regards, a thorough re-examination of  trade theory is necessary. Export oriented policy was successful, but we should know the conditions that it  would be successful.

In general, I feel there is a lack of interests in theory. I am now discussing the reason

Why did Eaton and Kortum model perform so badly? 

but there are very few reaction to this kind of questions. This facts may indicates a kind of apathy in theory. It is a shame.  

Dear Luis and Dear RG Fellows,

perhaps it is better to think positively. e.g. what one should do to improve research in developing countries.

I think that access to primary education, secondary schools and to the university is fundamental.

We must ensure that the poor have access to education. In all social classes, there is a percentage of talents and geniuses. Unfortunately many talents are dispersed due to the inability of poor families to keep their children to study. When poor parents make efforts they seek to direct children towards technical studies so as to favor the possibility of access to a job in the short term.

Richer countries should be expected to fund the study by them for a number of students coming from developing countries with the commitment to return to their country of origin and spread their knowledge or implement special development projects.

Best Regards

Ting Fa Marg

These are two definitions of discriminant analysis:

Discriminant analysis is a regression based statistical technique used in determining which particular classification or group (such as 'ill' or 'healthy') an item of data or an object (such as a patient) belongs to on the basis of its characteristics or essential features. It differs from group building techniques such as cluster analysis in that the classifications or groups to choose from must be known in advance.

Discriminant analysis is a form of multivariate analysis in which the objective is to establish a discriminate function. The function (typically a mathematical formula) discriminates between individuals in the population and allocates each of them to a group within the population. The function is established on the basis of a series of measurements or observations made on the individuals.

Applications

Linear Discriminant Analysis  is applied in positioning and product management. Other applications are the following:

Bankruptcy prediction

In bankruptcy prediction based on accounting ratios and other financial variables, linear discriminant analysis was the first statistical method applied to systematically explain which firms entered bankruptcy vs. survived. Despite limitations, including known non-conformance of accounting ratios to the normal distribution assumptions of LDA, Edward Altman's 1968 model is still a leading model in practical applications.

Face recognition

In computerized face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template. The linear combinations obtained using Fisher's linear discriminant are called Fisher faces, while those obtained using the related principal component analysis are called eigenfaces.

Marketing

In marketing, discriminant analysis was once often used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data. Logistic regression or other methods are now more commonly used. The use of discriminant analysis in marketing can be described by the following steps:

Formulate the problem and gather data — Identify the salient attributes consumers use to evaluate products in this category — Use quantitative marketing research techniques (such as surveys) to collect data from a sample of potential customers concerning their ratings of all the product attributes. The data collection stage is usually done by marketing research professionals. Survey questions, ask the respondent to rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes chosen by the researcher. Anywhere from five to twenty attributes are chosen. They could include things like: ease of use, weight, accuracy, durability, colourfulness, price, or size. The attributes chosen will vary depending on the product being studied. The same question is asked about all the products in the study. The data for multiple products is codified and input into a statistical program such as R, SPSS or SAS. (This step is the same as in Factor analysis).

Estimate the Discriminant Function Coefficients and determine the statistical significance and validity — Choose the appropriate discriminant analysis method. The direct method involves estimating the discriminant function so that all the predictors are assessed simultaneously. The stepwise method enters the predictors sequentially. The two-group method should be used when the dependent variable has two categories or states. The multiple discriminant method is used when the dependent variable has three or more categorical states. Use Wilks’s Lambda to test for significance in SPSS or F stat in SAS. The most common method used to test validity is to split the sample into an estimation or analysis sample, and a validation or holdout sample. The estimation sample is used in constructing the discriminant function. The validation sample is used to construct a classification matrix which contains the number of correctly classified and incorrectly classified cases. The percentage of correctly classified cases are called "the hit ratio".

Plot the results on a two dimensional map, define the dimensions, and interpret the results. The statistical program (or a related module) will map the results. The map will plot each product (usually in two-dimensional space). The distance of products to each other indicate either how different they are. The dimensions must be labelled by the researcher. This requires subjective judgement and is often very challenging. See perceptual mapping.

Biomedical studies

The main application of discriminant analysis in medicine is the assessment of severity state of a patient and prognosis of disease outcome. For example, during retrospective analysis, patients are divided into groups according to severity of disease – mild, moderate and severe form. Then results of clinical and laboratory analyses are studied in order to reveal variables which are statistically different in studied groups. Using these variables, discriminant functions are built which help to objectively classify disease in a future patient into mild, moderate or severe form.

In biology, similar principles are used in order to classify and define groups of different biological objects, for example, to define phage types of Salmonella enteritidis based on Fourier transform infrared spectra,]to detect animal source of Escherichia coli studying its virulence factors, etc.

Earth Science

This method can be used to separate the alteration zones. For example, when different data from various zones are available, discriminate analysis can find the pattern within the data and classify the them effectively

BR, I just checked and it does work. There must have a slow internet connection on your side. But it is also at https://www.researchgate.net/publication/266477435_On_Organizational_Configurations?ev=prf_pub. Else, please try again later.

I have just finished a research on how external interventions are not grounded on on-going social relations and structure and yet local culture (not limited to tribal cultures but also culture of national governments), norms, shared beliefs and conventions influence economic activities and transactions. IMF, WTO and WB tend to hand down policies which are thought to be applicable to anywhere and at the end there are applicable nowhere. Host governments cannot therefore, innovate to find other solutions.

I have worked for a local government in northern Uganda for over 13 years and was overseeing implementation of a world bank funded programme for social action fund. After two years those of us who interact directly with the poor communities realized the implementation framework was not feasible to generate the kind of expected outcomes. In a mid-term review we wanted the implementation model changed based on our observations and responses from the communities. We found stiff resistance from the programmers at the world bank who wrote the project. At the end of the project, little impact was found and the second phase had to be changed. A lot of what we suggested are now being implemented with making reference to our earlier criticism.

All I am emphasizing here is the role of local resources and knowledge in programming and implementation of interventions from outside is very vital in tailoring interventions to the real felt needs. But this requires trust from these organizations that there is local capacity to do so. The fact that people are surviving under those dire conditions means there are local resiliency and survival strategies on which external interventions can be grafted.   

OK, but as seen Russia will be the final winner of this "war game". So the result shall be that Russia will increase its power and influence over the EurAsEc countries .As a result the future seems optimistic.....

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Hello, I am involved in research regarding border management, particularly the role of frontex, smart border management and illegal migration, human trafficking. the european union is already externalizing border control to north african states to prevent migrants from leaving the shores and provides technical support regarding border management. an interesting question which crossed my mind when reading your article was: what is the impact of (inner) african border management on the migration towards europe?

Interesting question. The issue is whether we are talking about a 'funding relationship' or a 'partnership arrangement'. They are not the same thing. You can have a partnership without funding being a part of it, and you can have a funding relationship that is not really a partnership. My suggestion would be for an organization to first build a partnership based on trust (exchange of ideas, information, reciprocity) over a period, and then to explore funding once the value of the partnership has been explored and demonstrated.