The affiliation agreement in US broadcasting : The tie that binds

The affiliation agreement has historically been the mechanism through which television networks in the USA have solidified their power, erected barriers to entry for new networks, and taken over control of prime-time programming. While the FCC has modified certain contract terms, local affiliates still clear a staggering amount of network programmes. The central issue for public policy analysis is what can be done to take advantage of the economies of scale of networking without the accompanying side effects on new entry and programming decisions. The author explores what might happen if programme-by-programme bidding were substituted for the affiliation agreement.

US TV networks' response to new technology

The US television networks, so long the dominant force in US broadcasting, have seen their market shares and profits begin to fall as the new telecommunication services increase their penetration rates and fractionalize the viewing audience. In response to this competitive challenge, the networks have increased their offerings of news, bid high prices to retain the rights to professional sports, and decreased their reliance on theatrical movies. More importantly, the networks have themselves expanded into the new technologies in a number of different areas.TV networks New technology USA

Cohort profile: the Boston Area Community Health (BACH) survey.

The Boston Area Community Health (BACH) Survey is a community-based, random sample, epidemiologic cohort of n = 5502 Boston (MA) residents. The baseline BACH Survey (2002-05) was designed to explore the mechanisms conferring increased health risks on minority populations with a particular focus on urologic signs/symptoms and type 2 diabetes. To this end, the cohort was designed to include adequate numbers of US racial/ethnic minorities (Black, Hispanic, White), both men and women, across a broad age of distribution. Follow-up surveys were conducted ∼5 (BACH II, 2008) and 7 (BACH III, 2010) years later, which allows for both within- and between-person comparisons over time. The BACH Survey's measures were designed to cover the following seven broad categories: socio-demographics, health care access/utilization, lifestyles, psychosocial factors, health status, physical measures and biochemical parameters. The breadth of measures has allowed BACH researchers to identify disparities and quantify contributions to social disparities in a number of health conditions including urologic conditions (e.g. nocturia, lower urinary tract symptoms, prostatitis), type 2 diabetes, obesity, bone mineral content and density, and physical function. BACH I data are available through the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repositories (www.niddkrepository.org). Further inquiries can be made through the New England Research Institutes Inc. website (www.neriscience.com/epidemiology)

The relative impact of student affect on performance models in a spoken dialogue tutoring system

We hypothesize that student affect is a useful predictor of spoken dialogue system performance, relative to other parameters. We test this hypothesis in the context of our spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our system and corpora, which have been annotated with several student affective states, student correctness and discourse structure. We then discuss unigram and bigram parameters derived from these annotations. The unigram parameters represent each annotation type individually, as well as system-generic features. The bigram parameters represent annotation combinations, including student state sequences and student states in the discourse structure context. We then use these parameters to build learning models. First, we build simple models based on correlations between each of our parameters and learning. Our results suggest that our affect parameters are among our most useful predictors of learning, particularly in specific discourse structure contexts. Next, we use the PARADISE framework (multiple linear regression) to build complex learning models containing only the most useful subset of parameters. Our approach is a value-added one; we perform a number of model-building experiments, both with and without including our affect parameters, and then compare the performance of the models on the training and the test sets. Our results show that when included as inputs, our affect parameters are selected as predictors in most models, and many of these models show high generalizability in testing. Our results also show that overall, the affect-included models significantly outperform the affect-excluded models. © 2007 Springer Science+Business Media B.V