PubMed · 15361329
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Lucrèce. [Language].. https://doi.org/10.1051/medsci%2F2004208-9725
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Explore the source record for details and available documents.
OBJECTIVE: The purpose of this study was to determine whether the Motion Picture Association of America's ratings system distinguishes among the 3 primary rating categories (PG, PG-13, and R) with respect to violence based on a study of the 100 top-grossing films of 1994. METHODS: The Motion Picture Association of America assigns age-based ratings for every film that is released in the United States accompanied by the reasons for the rating. A data abstraction instrument was designed to code each act of violence within the sample of 100 films. A series of Poisson regression models were used to examine the association among rating, seriousness of violence, and primary reason for the rating assignment. RESULTS: The total average number of violent acts within each film by rating category increased from PG (14) to PG-13 (20) to R (32). However, using results from the Poisson models, it is clear that the rating does not predict the frequency of violence in films. For all 3 rating categories, the predicted number of violent acts is almost identical for films with violence as a primary descriptor and films with the highest level of seriousness (R = 62.4 acts, PG-13 = 55.2 acts, and PG = 56.1 acts). The regression analysis shows that the rating does not predict the frequency of violence that occurs in films. CONCLUSIONS: Frequency of violence alone is not the most important criterion for the assignment of rating. The content descriptors and average seriousness of films are better measures of the violence than rating assignment.
BACKGROUND: Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R. RESULTS: A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML. CONCLUSIONS: List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.