WWF Superstars Hd Full Movie Download
Tyler Bannowsky
Warhol superstars were a clique of New York City personalities promoted by the pop artist Andy Warhol during the 1960s and early 1970s.[1] These personalities appeared in Warhol's artworks and accompanied him in his social life, epitomizing his dictum, "In the future everyone will be famous for fifteen minutes". Warhol would simply film them, and declare them "superstars".[2]
The first recognised superstar was Baby Jane Holzer, whom Warhol featured in many of his early film experiments. The superstars would help Warhol generate publicity while Warhol offered fame and attention in return. Warhol's philosophies of art and celebrity met in a way that imitated the Hollywood studio system at its height in the 1930s and 1940s.[3]
Among the best-known of Warhol's superstars was Edie Sedgwick.[4] She and Warhol became very close during 1965 but their relationship ended abruptly early in the next year. Warhol would continue to associate himself with people including Viva, Candy Darling, Ultra Violet, Nico and International Velvet.
Warhol's studio, The Factory, played host to most of his superstars and as his experiments in film continued he became more interested in the bohemian eccentrics attracted to the studio.[5] Some of the most important superstars to emerge from the period of the first Factory (known as the 'Silver Factory' because silver foil had been applied to the walls and ceilings) include Paul America, Ondine, Taylor Mead, Rolando Peña, Mary Woronov, Eric Emerson, Gerard Malanga, Billy Name, Brigid Berlin and Sappheo.
In the later films, made in collaboration with Paul Morrissey, Warhol brought in new superstars including Joe Dallesandro, Penny Arcade, Andrea Feldman, Jane Forth, Geraldine Smith, and Sylvia Miles. During this period Warhol developed an increasing fascination with trans women and drag queens, and promoted Candy Darling, Holly Woodlawn and Jackie Curtis to superstar status.
We find superstars exist not only among firms but among sectors and cities as well, although the trend is most evident among cities and firms. Relative to peers, superstars share several common characteristics. In addition to capturing a greater share of income and pulling away from peers, superstars exhibit relatively higher levels of digitization; greater labor skill and innovation intensity; more connections to global flows of trade, finance, and services; and more intangible assets than do their peers. Yet there are some variations. We find a higher churn rate among superstar firms compared to cities, indicating higher levels of persistence among superstar cities.
At the same time, a notable number of superstar cities (and not just the city-states) have a disproportionate share of their national income given their share of the population. In addition to the 50 global superstars, we identify more than 75 regional superstar cities that are smaller but share many of these characteristics and could become global economic hubs in the future.
Our analysis so far raises questions for further research. For instance, we find that many suggested explanations of the superstar effect, such as productivity growth, technological or regulatory advantage, and intangible investments, do not fully or individually account for the phenomenon. What combination of factors leads to the emergence of superstar firms, sectors, and cities? How much of the superstar effect among firms is due to changes in the macroeconomy, including changes in value associated with different types of inputs and outputs, or to the wider accessibility of large global markets and low interest rates? How much is due to firm-specific investments in R&D and intangibles? What is the economic impact, both positive and negative, of superstars on innovation and competition, jobs and wages, investment and productivity, growth of smaller firms, consumer surplus, and overall prosperity and inclusive growth?
The growth of superstar firms, sectors, and cities also creates policy questions beyond the causes of superstars and their effects on competition and market structure. These considerations include implications for inclusive economic growth that can support and sustain broad-based employment and wage growth.
The findings in this paper are by no means the last word on the topic of superstars. Indeed, we have highlighted questions that require further research to inform smart policies by policy leaders and winning strategies by business leaders, all with the goal of not only value creation but also more inclusive growth and shared prosperity.
Academic superstars are a familiar phenomenon. These academics write the papers that everyone reads and talks about, they make media appearances, give presidential addresses, and they win grants and awards. The work of an academic superstar generally attracts more attention than that of the average academic.
In this paper I raise the question whether the phenomenon of stratification serves the aims of academic science.Footnote 2 I argue that the answer to this question depends largely on how one thinks academic superstars are distinguished from academic nobodies. In particular, what are the roles of competence and luck in distinguishing the two groups?
In contrast, Sect. 6 focuses on the less optimistic lessons that can be drawn from the model. I show that the model is also consistent with a scenario in which stratification is determined by a large component of randomness. Because they cannot be distinguished by their past performance, it is impossible to separate the lucky superstars from the competent ones. But the epistemic benefit of stratification derives exactly from the ability to identify competent academics, or so I argue. It follows that stratification either hinders the aims of academic science, or, if it does help, it is impossible to show this.
Suppose a group of scientists are interested in the same experiments. Suppose also that each scientist has a fixed amount of time to perform experiments; perhaps they are all in a one-year post-doc and they need to produce a paper at the end of it. Suppose further that the more competent a scientist is, the more replications she can perform in that time. And suppose finally that the work of a scientist who has performed more replications receives more attention. It follows that the superstarsFootnote 3 in this toy model are exactly the most competent scientists.
Suppose as before that the work of those scientists who performed more replications receives more attention. Since in this toy model the only difference between the scientists is in the probabilistic behavior of their equipment, the superstars are those lucky scientists for whom all n replications succeed (which happens with probability \(\alpha ^n\) if the success probabilities are independent).
This will be the key difference between competence and luck in the discussion of competent and lucky superstars in Sects. 5 and 6. Competent superstars are those for whom good past academic work is predictive of good future academic work, while lucky superstars are those for whom good past academic work is not indicative of the value of their future work.Footnote 4
Suppose as before that scientists have time to perform n replications, each of which succeeds or fails independently from the others with some fixed probability. Competence is reflected in the value of that probability. Suppose further that there are just two types of scientists: average scientists, whose success probability for each replication is \(\alpha \), and good scientists, whose success probability is \(\beta \) (\(0< \alpha< \beta < 1\)). As before the superstars are those scientists for whom all n replications succeed.
In this section I show that the results from the previous section are consistent with a purely competence-based view of academic superstars. I argue for two consequences of this insight. First, one should not be too quick in concluding that particular patterns of stratification could not have resulted from differences in competence. Second, if the competence-based view is correct stratification has some important benefits. The next section considers the flip side of these arguments.
Stratified In-Degrees, the main result of Sect. 4, identified differences in the epistemic value of papers as a source of differences in the number of times they get read (their in-degree in the network). Because differences in competence can create differences in epistemic value, the result is consistent with a view on which only the most competent academics become superstars.
Toy Model 1 in Sect. 2 illustrates this. Differences in competence can create differences in the number of replications each academic is able to do, and by Stratified In-Degrees the most competent academics will be superstars.
Speaking more generally, on any interpretation of competence on which more competent academics tend to write papers of higher epistemic value (on either the strict or the loose interpretation), the competent academics will be the superstars. By Exponential In-Degrees, differences in epistemic value may be enlarged exponentially in terms of the amount of attention paid to academic work, as measured in, e.g., citation or productivity metrics.
They would thus use the results of regression analyses to reject the competence-based view of superstars I outlined. But this conclusion is too quick. The fact that a model in which competence and citations (or productivity) stand in a linear relation cannot explain all of the variance in citations does not rule out the possibility that a model using a nonlinear relation can. According to my results this may be exactly what is needed.
For the remainder of this section, suppose that the competence-based view of superstars I outlined is correct (cf. Cole and Cole 1973; Rosen 1981; Strevens 2006). That is, papers of high epistemic value tend to get read the most, competent academics tend to produce papers of high epistemic value, and therefore academic superstars are more competent than academic nobodies.
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