Film and TV – week 1

In 200 words or less please outline your goals, desires – what you want to get out of this semester. You will review this later in the course.

My goals for this semester include being more active in the filming and editing process. I tend to lean more towards organising the group I’m in and screen writing. I want to be more confident using equipment and know the set up I need to get the audio and visual results I’m after.

And although I enjoy the writing process, I haven’t yet written for screen, or any type of script, so I look forward to writing something that can be translated onscreen. I think that I will learn a lot this semester.

Consider  Jasmine’s lecture on Screenwriting and briefly describe one point that you have taken from it.

One point I took away from the lecture was about creative a character. I am quite drawn to characters that are relatable and ‘human’/average. However Jasmine make me realise that these qualities aren’t enough to make a character interesting to watch and drive the story.

If they want something badly enough, or do unexpected things, it makes for a better plot. Characters should be bold and decisive – this helps to drive the story.

Select from one of the readings from week 1 or 2 and briefly describe two points that you have taken from that reading. Points that excite you, something that was completely new to you.

Reading: Getting An Idea, Robin Plunkett

1. An idea for a film can root from anywhere – a photograph, a person you know, a news story etc. You don’t have to know what plot you want to tell to start.

2. French filmmaker Jacques Rivette started making a film by identifying budget and restrictions, and finding a script that would work within those limitations. I found this interesting and relevant for our semester assignment as we need to produce something on a non-existent budget and it can only be 5 minutes long.

 

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Week 11 Reading

Schultz, Pit. Latour, Bruno: On Actor Network Theory: A Few Clarifications 1/2

The Actor-Network Theory (ANT)

Misuse of the term ‘networks’ has lead to misunderstandings within the actor-network theory. For example:

1. Networks are given a technical meaning, such as a train network or telephone network. A technical network in this state is only one of the possible final and stabilised state of an actor-network.

2. The ANT has little to do with the study of social networks. The word actor, or actant, is extended to non-human, non individual entities. Whereas social network adds information on the relations of humans in the social and natural world. Social networks are included in ANT, but are not prominent or seen as of greater importance.

The ANT claims that modern societies cannot be described without recognising them as having a fibrous character that is never captured by existing theories without being influenced by their existing politics, layers, or territories.

Not that kind of social network
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Week 10 Reading: Manovich

“Database as Symbolic Form” by Manovich.

New media objects do not nescessariry tell stories, they are instead correlated as databases and don’t have a traditional beginning or end. They are void of any form of element organisation or structure. Instead, they are collections of individual items.

Database: a structured collection of data. Data is stored and organised for fast searching and retrieval by a computer.

Databases can be hierarchical, a network, relational, and object-orientated; using different models to organise data.

 Web page: a sequential list of separate elements: text blocks, images, and links to other pages.

Online, pages are ever-growing and evolving. New elements and links can be edited and added at any time, making the Web an anti narrative made up of collections, not a story. As Manovich asks, “how can one keep a coherent narrative or any other development trajectory through the material if it keeps changing?”

Algorithms and data structures have a symbolic relationship. The more complex the data structure of a computer program, the simpler the algorithm needs to be, and vise versa. According to a computer, data structures and algorithms are two halves of the ontology of the world. In contrast, narrative forms do not require algorithm-like behaviour from their readers.

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Week 9 Reading: ‘Culture’ and ‘Technology’

Murphie, Andrew, and John Potts. Culture and Technology. New York: Palgrave Macmillan, 2003. Print.

Culture and technology incorporate many interests and disciplines within their dynamic field. Both are prone to rapid change and constant growth.

Technoculture therefore needs to be assessed in a similar, dynamic way when relating it to theoretical perspectives.

Technology: (tekhne – craft, logos – system)
The application of a body of knowledge, or science, in specific areas. Also defined as the ability to measure, predict, and control natural forces.

Technique:  the use of skill to accomplish something. Technique is what makes technology useful to us, if we didn’t know how to operate technology, it would lose it’s value.

Culture: Is difficult to define. generally, it involves all human activity around the world. It’s specific meaning refers to self-contained cultures, such as ‘French culture’. It brackets off into other areas such as news, finance, sports etc.

Each of these definitions is prone to change and development, so their core definitions will also change over time. Technology plays a crucial role in the large-scale and popular forms of culture.

 

 

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Bear

Watched this short film by Nash Edgerton this week in Cinema. I love!

http://www.youtube.com/watch?v=CKeooK0zfeU

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Recommended for you…

I found this blog post from Adrian really interesting as he explained how recommendation generators work. I am usually insulted by the dating website and dieting ad’s that pop up on my Facebook newsfeed as ‘recommended for you’, however I am enjoying Spotify Radio.

Spotify will take a track that you’re listening to and start a radio program (/playlist) of songs you might like based on the original song. Most of these tracks seem to be from similar artists or from a similar genre of music, but I have found it to be a new way to discover some sweet new tunes 🙂

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Week 8 Reading: Barabási “Rich Get Richer”

Barabási, Albert-László. “Rich Get Richer”

The random model of Erdos and Renyi rests on tro simple and often disregarded assumptions.
The web is always growing, and therefore cannot be assumed to be static when analysing its structure.
Most networks share this essential feature of growth.

Model A. nodes link randomly to one another. The longer a node is in the web, the more time it has to gain links.

However, links aren’t (always) random, as computer users select the nodes/pages that they want to visit. For example, you have a choice of which website to visit in a Google search, or you may be taken to a random page in the search results. I tend to agree with Barabasi here when he says he doesn’t think anyone ever uses this option.

The better known a website is, the more links it acquires and it can therefore be referred to as a hub. Users prefer to link to the better connected hub – while our individual choices are highly unpredictable, as a group we follow strict patterns.

Real networks are governed by two laws:
growth – for each given period of time we add a new node to the network. This step underscores the fact that the networks are assembled one node at a time.
preferential attachment – We assume that each new node connects to the existing nodes with two links. It is twice as likely that a newer node will connect to the more connected node when given a choice.

Each network starts from a small nucleus and expands with the addition of new nodes.

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