7 Economic bases of communication

Siegert, Gabriele; Rimscha, M. Bjørn von

doi:10.1515/9783110240450.123

Cited by 8 publications

(12 citation statements)

References 30 publications

(33 reference statements)

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“…A significant simplification is that for gaussian fields, the joint probability distribution for m(1) and m(2) factors into a product of separate distributions of the form (123) for each component. This results is an equation of form (138) for any n, but with the function C(γ) given by (131) for general n instead of (126). Again, a(t) = λ/2t, with λ chosen to eliminate the power-law tail in the scaling function f (x).…”

Section: Mazenko's Methodsmentioning

confidence: 99%

“…To summarise, the virtues of Mazenko's approach are (i) only the assumption that the field m is gaussian is required, (ii) the scaling function has a non-trivial dependence on d (whereas, apart from the trivial dependence through the diffusion constant D, (122), (126) and (131) are independent of d), and (iii) the non-trivial behaviour of different-time correlation functions [20] emerges in a natural way [96]. In addition, the OJK result (126), and its generalisation (131), are reproduced for d → ∞, while the exact scaling function (108) of the 1−d Glauber model is recovered from (139) in the limit d → 1 [99]. In practice, however, for d ≥ 2 the shape of the scaling function f (x) differs very little from that of the OJK function given by (126) and (122), or its generalization (131) for vector fields [60].…”

Section: Mazenko's Methodsmentioning

confidence: 99%

“…The only difference between (138) and (193) is the extra (−∇ 2 ) on the right-hand side. The form (193) is obtained for any n, with the function C(γ) given by (131). If we seek a scaling solution of (193), of the form C(r, t) = f (r/L(t)), it is immediately clear that consistency requires a(t) ∼ 1/L(t) 2 and L(t) ∼ t 1/4 .…”

Section: The Mazenko Methods For Conserved Vector Fieldsmentioning

confidence: 99%

“…Then C(12) = m(1) ·m(2) in the scaling regime. The required gaussian average over the fields m(1), m(2) yields the BPT scaling function (131). Again, it can be systematically improved by expanding in 1/N.…”

Section: Vector Fieldsmentioning

confidence: 99%

“…The theoretical curves in Figure 14 are the BPT function (131), which reduces to the OJK scaling function for n = 1. In making the fits, γ was replaced by exp(−αr 2 /L(t) 2 ) with the scale factor α(n, d) adjusted to give the best fit by eye.…”

Section: The Porod Tailmentioning

confidence: 99%

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Theory of phase-ordering kinetics

Bray¹

2002

Advances in Physics

752

1,199

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The theory of phase ordering dynamics -the growth of order through domain coarsening when a system is quenched from the homogeneous phase into a brokensymmetry phase -is reviewed, with the emphasis on recent developments. Interest will focus on the scaling regime that develops at long times after the quench. How can one determine the growth laws that describe the time-dependence of characteristic length scales, and what can be said about the form of the associated scaling functions? Particular attention will be paid to systems described by more complicated order parameters than the simple scalars usually considered, e.g. vector and tensor fields. The latter are needed, for example, to describe phase ordering in nematic liquid crystals, on which there have been a number of recent experiments. The study of topological defects (domain walls, vortices, strings, monopoles) provides a unifying framework for discussing coarsening in these different systems.

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Section: Mazenko's Methodsmentioning

confidence: 99%

Section: Mazenko's Methodsmentioning

confidence: 99%

Section: The Mazenko Methods For Conserved Vector Fieldsmentioning

confidence: 99%

Section: Vector Fieldsmentioning

confidence: 99%

Section: The Porod Tailmentioning

confidence: 99%

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Theory of phase-ordering kinetics

Bray¹

2002

Advances in Physics

752

1,199

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Advertising‐Supported Journalism

Siegert

2019

The International Encyclopedia of Journalism Studies

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For decades, journalism and the advertising industry have been closely affiliated with each other. Research relates to this mutual interdependence with concepts of the mixed financing of media products and two‐sided markets. For various reasons, the prices for journalism paid by the users do not bear the running costs for journalism and media companies have to find other sources of income, mainly advertising. Therefore, media companies operate in a dual‐product market. In generating and distributing journalistic content they also produce audience attention, which they sell to the advertising industry. Although it is still an economic necessity, the journalism–advertising connection is criticized as it leads to advertiser influence on editorial decision making and journalistic routines, with a massive impact on news coverage. However, convergence and changes in the way advertising works and the way advertising uses online and mobile platforms as vehicles summarize the major challenges to advertising‐supported journalism.

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