Электронный учебно-методический комплекс по учебной дисциплине Философия и методология науки для студентов, слушателей, осваивающих содержание образовательной программы высшего образования 2 ступени

3.Motley crew principle: For relatively complex creative products, the production requires diversely skilled inputs. Each skilled input must be present and perform at some minimum level to produce a valuable outcome.

4.Infinite variety: Products are differentiated by quality and uniqueness; each product is a distinct combination of inputs leading to infinite variety options.

5.A list/B list: Skills are vertically differentiated. Artists are ranked on their skills, originality, and proficiency in creative processes and/or products. Small differences in skills and talent may yield huge differences in success.

6.Time flies: When coordinating complex projects with diversely skilled inputs, time is of the essence.

7.Ars longa: Some creative products have durability aspects that invoke copyright protection, allowing a creator or performer to collect rents.

The properties described by Caves have been criticized for being too rigid. Not all creative workers are purely driven by 'art for art's sake'. The 'ars longa' property also holds for certain noncreative products. The 'time flies' property also holds for large construction projects. Creative industries are therefore not unique, but they score generally higher on these properties relative to non-creative industries.

There is often a question about the boundaries between creative industries and the similar term of cultural industries. Cultural industries are best described as an adjunct-sector of the creative industries. Cultural industries include industries that focus on cultural tourism and heritage, museums and libraries, sports and outdoor activities, and a variety of 'way of life' activities that arguably range from local pet shows to a host of hobbyist concerns. Thus cultural industries are more concerned about delivering other kinds of value – including cultural wealth and social wealth – rather than primarily providing monetary value. Some authors, such as the economist Richard Florida, argue for a wider focus on the products of knowledge workers, and judge the 'creative class' to include nearly all those offering professional knowledge-based services.

The term creative industries begins to elide with knowledge economy and questions of intellectual property ownership in general. Florida's focus leads him to pay particular attention to the nature of the creative workforce. In a study of why particular US cities such as San Francisco seem to attract creative producers, Florida argues that a high proportion

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of workers from the 'creative class' provide a key input to creative production, which enterprises seek out. He seeks to quantitatively establish the importance of diversity and multiculturalism in the cities concerned, for example the existence of a significant public gay community, ethnic and religious variety, and tolerance.

Globally, Creative Industries excluding software and general scientific research and development are said to have accounted for around 4% of the world's economic output in 1999, which is the last year for which comprehensive figures are currently available. Estimates of the output corresponding to scientific Research and Development suggest that an additional 4-9% might be attributable to the sector if its definition is extended to include such activities, though the figures vary significantly between different countries.

Taking the UK as an example, in the context of other sectors, the creative industries make a far more significant contribution to output than hospitality or utilities and deliver four times the output due to agriculture, fisheries and forestry. In terms of employment and depending on the definition of activities included, the sector is a major employer of between 4-6% of the UK's working population, though this is still significantly less than employment due to traditional areas of work such as retail and manufacturing.

Within the creative industries sector and again taking the UK as an example, the three largest sub-sectors are design, publishing, and television and radio. Together these account for around 75% of revenues and 50% of employment. The complex supply chains in the creative industries sometimes make it challenging to calculate accurate figures for the gross value added by each sub-sector. This is particularly the case for the service-focused sub-sectors such as advertising, whereas it is more straightforward in product-focused sub-sectors such as crafts. Not surprisingly, perhaps, competition in product-focused areas tends to be more intense with a tendency to drive the production end of the supply chain to become a commodity business.

There may be a tendency for publicly funded creative industries development services to inaccurately estimate the number of creative businesses during the mapping process. There is also imprecision in nearly all tax code systems that determine a person's profession, since many creative people operate simultaneously in multiple roles and jobs. Both these factors mean that official statistics relating to the Creative Industries should be treated with caution.

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The creative industries in Europe make a significant contribution to the EU economy, creating about 3% of EU GDP - corresponding to an annual market value of €500 billion - and employing about 6 million people. In addition, the sector plays a crucial role in fostering innovation, in particular for devices and networks. The EU records the second highest TV viewing figures globally, producing more films than any other region in the world. In that respect, the newly proposed 'Creative Europe' programme will help preserve cultural heritage while increasing the circulation of creative works inside and outside the EU. The programme will play a consequential role in stimulating cross border cooperation, promoting peer learning and making these sectors more professional. The Commission will then propose a financial instrument run by the European Investment Bank to provide debt and equity finance for cultural and creative industries. The role of the non-state actors within the governance regarding Medias will not be neglected anymore. Therefore, building a new approach extolling the crucial importance of a European level playing field industry may boost the adoption of policies aimed at developing a conducive environment, enabling European companies as well as citizens to use their imagination and creativity – both sources of innovation, and therefore of competitiveness and sustainability. It supposes to tailor the regulatory and institutional frameworks in supporting private-public collaboration, in particular in the Medias sector. The EU therefore plans to develop clusters, financing instruments as well as foresight activities to support this sector. The European Commission wishes to assist European creators and audiovisual enterprises to develop new markets through the use of digital technology, and asks how policy-making can best help achieve this. A more entrepreneurial culture will have to take hold with a more positive attitude towards risktaking, and a capacity to innovate anticipating future trends. Creativity plays an important role in human resource management as artists and creative professionals can think laterally. Moreover, new jobs requiring new skills created in the post-crisis economy should be supported by labour mobility to ensure that people are employed wherever their skills are needed.

In the introduction to a 2013 special issue of Work and Occupations on artists in the US workforce, the guest editors argue that by examining the work lives of artists, one can identify characteristics and actions that help both individual workers and policy makers adapt to changing economic conditions. Elizabeth Lingo and Steven Tepper cite multiple

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sources to suggest artists' skill sets allow them to work beyond existing markets and create entirely new opportunities for themselves and others. Specifically, Lingo and Tepper suggest artistic workers are "catalysts of change and innovation because they face special challenges managing ambiguity, developing and sustaining a relative identity, and forming community in the context of an individually based enterprise economy. Because of these adaptive skills, the suggestion is that studying how artists cope with uncertainty and the factors that influence their success should be relevant for understanding these broader social and economic trends facing today's workforce.

This view of artist-as-change-agent changes the questions researchers ask of creative economies. Old research questions would focus on topics like skills, work practices, contracts, wage differentials, employment incentives, formal credentials, employment pipelines, and labor flows of differentiated occupational categories. Examples of new questions include:

1.How do artists both create changes in the labor market itself and the way cultural work is done?

2.What is their process of innovation and enterprise?

3.What is the nature of their work and the resources they draw upon?

4.How do different network structures produce different opportunity spaces?

5.How do artistic workers create and manage planned serendipity – the spaces and exchanges that produce unexpected collaborations and opportunities?

6.How do creative workers broker and synthesize across occupational, genre, geographic, and industry boundaries to create new possibili-

ties?

As some first world countries struggle to compete in traditional markets such as manufacturing, many now see the creative industries as a key component in a new knowledge economy, capable perhaps of delivering urban regeneration, often through initiatives linked to exploitation of cultural heritage that leads to increased tourism. It is often argued that, in future, the ideas and imagination of countries like the United Kingdom will be their greatest asset; in support of this argument, a number of universities in the UK have started to offer creative entrepreneurship as a specific area for study and research. Indeed, UK government figures reveal that the UK's creative industries account for over a

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million jobs and brought in £112.5 billion to the UK economy, although the data sets underlying these figures are open to question.

In recent years, creative industries have become 'increasingly attractive to governments outside the developed world'. In 2005, the United Nations Conference on Trade and Development XI High Level Panel on Creative Industries and Development commissioned several studies to identify challenges and opportunities facing the growth and development of creative industries in developing industries. As Cunningham et al. Рut it, 'the harnessing of creativity brings with it the potential of new wealth creation, the cultivation of local talent and the generation of creative capital, the development of new export markets, significant multiplier effects throughout the broader economy, the utilisation of information communication technologies and enhanced competitiveness in an increasingly global economy'. A key driver of interest in creative industries and development is the acknowledgement that the value of creative production resides in ideas and individual creativity, and developing countries have rich cultural traditions and pools of creative talent which lay a basic foundation for creative enterprises. Reflecting the growing interest in the potential of creative industries in developing countries, in October 2011 a Ministry of Tourism and Creative Economy was created within the Indonesian government with well-known economist Dr Mari Pangestu appointed as the first minister to hold the position.

7.1.21. Method of the science

The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning. The scientific method as a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses. Experiments need to be designed to test hypotheses. The most important part of the scientific method is the experiment.

The scientific method is a continuous process, which usually begins with observations about the natural world. Human beings are naturally inquisitive, so they often come up with questions about things they see or hear and often develop ideas about why things are the way they are. The best hypotheses lead to predictions that can be tested in various

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ways, including making further observations about nature. In general, the strongest tests of hypotheses come from carefully controlled and replicated experiments that gather empirical data.

Depending on how well the tests match the predictions, the original hypothesis may require refinement, alteration, expansion or even rejection. If a particular hypothesis becomes very well supported a general theory may be developed. Although procedures vary from one field of inquiry to another, identifiable features are frequently shared in common between them. The overall process of the scientific method involves making conjectures, deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions. A hypothesis is a conjecture, based on knowledge obtained while formulating the question. The hypothesis might be very specific or it might be broad. Scientists then test hypotheses by conducting experiments. Under modern interpretations, a scientific hypothesis must be falsifiable, implying that it is possible to identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested.

The purpose of an experiment is to determine whether observations agree with or conflict with the predictions derived from a hypothesis. Experiments can take place anywhere from a college lab to CERN's Large Hadron Collider. There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, it represents rather a set of general principles. Not all steps take place in every scientific inquiry, and are not always in the same order. Some philosophers and scientists have argued that there is no scientific method, such as Lee Smolin and Paul Feyerabend.

The scientific method is the process by which science is carried out. As in other areas of inquiry, science can build on previous knowledge and develop a more sophisticated understanding of its topics of study over time.This model can be seen to underlay the scientific revolution. One thousand years ago, Alhazen argued the importance of forming questions and subsequently testing them, an approach which was advocated by Galileo in 1638 with the publication of Two New Sciences. The current method is based on a hypothetico-deductive model formulated in the 20th century, although it has undergone significant revision since first proposed.

The overall process involves making conjectures, deriving predictions from them as logical consequences, and then carrying out experi-

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ments based on those predictions to determine whether the original conjecture was correct. There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, they are better considered as general principles. Not all steps take place in every scientific inquiry, and are not always in the same order. As noted by William Whewell, invention, sagacity, genius are required at every step.

The question can refer to the explanation of a specific observation, as in "Why is the sky blue?", but can also be open-ended, as in "How can I design a drug to cure this particular disease?" This stage frequently involves finding and evaluating evidence from previous experiments, personal scientific observations or assertions, and/or the work of other scientists. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.

A hypothesis is a conjecture, based on knowledge obtained while formulating the question. That may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein's equivalence principle or Francis Crick's DNA makes RNA makes protein, or it might be broad, e.g., unknown species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.

This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The more unlikely that a prediction would be correct simply by coincidence, then the more convincing it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not al-

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ready known, due to the effects of hindsight bias. Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. These statements about the relative strength of evidence can be mathematically derived using Bayes' Theorem.

This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from a hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk. Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests.

Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider, at the bottom of an ocean, on Mars, and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.

This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is

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not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and experience are frequently incorporated at any stage in the process. Depending on the complexity of the experiment, many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question. The scientific method also includes other components required even when all the iterations of the steps above have been completed.

If an experiment cannot be repeated to produce the same results, this implies that the original results might have been in error. As a result, it is common for a single experiment to be performed multiple times, especially when there are uncontrolled variables or other indications of experimental error. For significant or surprising results, other scientists may also attempt to replicate the results for themselves, especially if those results would be important to their own work.

The process of peer review involves evaluation of the experiment by experts, who typically give their opinions anonymously. Some journals request that the experimenter provide lists of possible peer reviewers, especially if the field is highly specialized. Peer review does not certify correctness of the results the experiments themselves were sound. If the work passes peer review, which occasionally may require new experiments requested by the reviewers, it will be published in a peerreviewed scientific journal. The specific journal that publishes the results indicates the perceived quality of the work.

Scientists typically are careful in recording their data, a requirement promoted by Ludwik Fleck and others.Though not typically required, they might be requested to supply this data to other scientists who wish to replicate their original results, extending to the sharing of any experimental samples that may be difficult to obtain.

Scientific inquiry generally aims to obtain knowledge in the form of testable explanations that scientists can use to predict the results of future experiments. This allows scientists to gain a better understanding of the topic under study, and later to use that understanding to intervene in its causal mechanisms (such as to cure disease). The better an explanation is at making predictions, the more useful it frequently can be, and the more likely it will continue to explain a body of evidence better than its alternatives. The most successful explanations – those which explain

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and make accurate predictions in a wide range of circumstances – are often called scientific theories.

Most experimental results do not produce large changes in human understanding; improvements in theoretical scientific understanding typically result from a gradual process of development over time, sometimes across different domains of science. Scientific models vary in the extent to which they have been experimentally tested and for how long, and in their acceptance in the scientific community. In general, explanations become accepted over time as evidence accumulates on a given topic, and the explanation in question proves more powerful than its alternatives at explaining the evidence. Often subsequent researchers reformulate the explanations over time, or combined explanations to produce new explanations.

Tow sees the scientific method in terms of an evolutionary algorithm applied to science and technology. Scientific knowledge is closely tied to empirical findings, and can remain subject to falsification if new experimental observation incompatible with it is found. That is, no theory can ever be considered final, since new problematic evidence might be discovered. If such evidence is found, a new theory may be proposed, or it is found that modifications to the previous theory are sufficient to explain the new evidence. The strength of a theory can be argued to relate to how long it has persisted without major alteration to its core principles.

Theories can also become subsumed by other theories. For example, Newton's laws explained thousands of years of scientific observations of the planets almost perfectly. However, these laws were then determined to be special cases of a more general theory, which explained both the exceptions to Newton's laws and predicted and explained other observations such as the deflection of light by gravity. Thus, in certain cases independent, unconnected, scientific observations can be connected to each other, unified by principles of increasing explanatory power.

Since new theories might be more comprehensive than what preceded them, and thus be able to explain more than previous ones, successor theories might be able to meet a higher standard by explaining a larger body of observations than their predecessors. For example, the theory of evolution explains the diversity of life on Earth, how species adapt to their environments, and many other patterns observed in the natural world; its most recent major modification was unification with genetics to form the modern evolutionary synthesis. In subsequent modifications,

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