Wiki/Bartholomaeus Traubeck, Guy Be Good / Ersions, 2012

Trimester 2, 2012.

Description

'Guy Be Good': For the Archive & Memory thematic I chose to work with mainstream cinema and how it creates wishes and embeds them into our collective memory. This was partly inspired by Arjun Appadurai's text Archive and Aspiration in which he also talks about the aspirations western cinema (and TV) raises in migrants from poorer, non-western countries.

"Migrants, especially the poorer migrants of this world, are not thriving in a world of free markets, consumer paradise or socìal liberation. They are struggling to make the best of the possibilities that are opened to them in the new relation- ships between migration and mass mediation. There is no doubt that migrants today, as migrants throughout human history, move either to escape horrible lives, to seek better ones, or both. The only new fact in the world of electronic mediation is that the archive of possible lives is now richer and more available to ordinary people than ever before. Thus, there is a greater stock of material from which ordinary people can craft the scripts of possible worlds and imagined selves. This does not mean that the social projects that emerge from these scripts are always liberating or even pleasant. But it is an exercise in what I have recently called ‘the capacity to aspire'." - Arjun Appadurai

Mainstream cinema as part of this archive of possible lives seems like a good starting point, as it usually encompasses the 'great' stories and all their necessary parts like love, identity, morality, ethics, drama etc. Also its language is generally very basic making it easy for any kind of audience to understand.

In order to find an approach to analyse the language and vocabulary of mainstream cinema, I downloaded the English subtitle files of the Top 50 movies of the ‘Top-US-Grossing Feature Films Released In 2011‘ list on IMDB. These subtitle files were then skimmed for word occurence. The results are divided into substantives, verbs and adjectives, the basic building blocks of a sentence.

Originally I wanted to do a more complex work, including a sentence by sentence analysis and in the end the creation of a prototypical script synthesized automatically from the source subtitle files. This approach was unfortunately not possible to realize due to several reasons. First of all the quality of the subtitle files is very diverse and choosing the best translation can almost only be done by humans. Also the sentence by sentence analysis and the synthesis of texts was too complex for my understanding of programming and not achievable in the two left over months I had after the idea came about.

After the assessment: During the assessment a few flaws and pssoble improvements for the project came about. First of all there is a problem with subtitle files since they only contain the spoken content of the movie and no action-directed instructions. Getting the original scripts could produce quite different outcomes. Another point was that there should be a thorough analysis of meaning and not of indivual words, maybe in terms of actions or realtions inside a certain amount of words.

Another proposed direction was to make it more personal and maybe try to write a script on the basis of this raw analysis. Maybe a script that encompasses the most used words or something along the lines of that.

A direct improvement to the actual presented work would be to repeat the process with different genres of movies. It could be interesting to juxtapose the 50 best selling films with something else. This could also verify the thesis that the outcome is the vocabulary of mainstream cinema (or negate it as well).

I think that for me the next thing to do is to first follow the last proposition and try to repeat with different genres/groups of films. A problem will be getting the subttitle files as it was not easy to get them for the 50 best-selling movies of 2011 already. after that I would like to try to go deeper and try to approach the theme in terms of basic semantic relations/meanings. Simon's suggestion was to try to find simple actions on the basis of "what does what with what" (subject - action - object) structures.

Media

Photos

• At least 2, Max 5.
• native resolution only (that is the highest resolution you have for your photo and whatever resolution if you make screencaps)
• PNG format

Video

optional unless the final outcome of the project, or one of its main components, is a video/film/animation.

• allowed containers/codecs combos:
  • WebM (preferred):
    • Video codec: VP8 (3-5 Mbps data rate)
    • Audio codec: Vorbis (-q 6)
  • Ogg:
    • Video codec: Theora (3-5 Mbps data rate)
    • Audio codec: Vorbis (-q 6)
  • MP4:
    • Video Codec: H.264 (3-5 Mbps data rate)
    • Audio Codec: AAC (160kbps or 384kbps if 5.1) or MP3 (-V0 or -b 320)
• resolution: 720p if source >= 720p, native resolution otherwise, progressive.
• FPS: 24, 25, 30.
• Sample rate:
  • 44.1kHz if source is 44.1kHz or 88.2kHz
  • 48 kHz if source is 48kHz or 96kHz
  • 44.1 kHz for everything else.

Audio

optional unless the final outcome of the project, or one of its main components, is a sound piece/track.

• allowed containers/codecs combos:
  • Ogg: Vorbis (-q 6) preferred
  • MPEG: MP3 (-V0 or -b 320)
  • FLAC (-8)
• Sample rate:
  • 44.1kHz if source is 44.1kHz or 88.2kHz
  • 48 kHz if source is 48kHz or 96kHz
  • 44.1 kHz for everything else.
• Do not transcode! For instance, if the sound generated is directly an MP3, do not re-encode it with the settings above or with a different codec. Only encode original uncompressed or lossless compressed sources.

Additional Information

optional

• Project URL (if lives on an external site)
• extra wiki links (in case you have relevant notes/journals/documentation in your User: page, this is useful particularly if you have been asked to articulate further or refine your project during your assessment)
• Animated GIFs