10 Matching Annotations
  1. Sep 2018
    1. Whilespatial biases may contribute to these findings,asnodes belonging to the same module tend to be anatomically colocalized [7,8],they cannot explain these effects entirely [94,95].

      Very nice review. Please note the reference [94] (Pantazatos et al.) is misplaced because they did not argue that spatial biases cannot entirely explain the putative links between CGE and functional segregation. Instead, they argued there was insufficient evidence in the original Richiardi et al. study linking elevated CGE with resting state functional networks, and that spatial biases may in fact entirely account for their findings. To describe the debate/exchange more accurately, I would suggest replacing the below sentence

      “While spatial biases may contribute to these findings, as nodes belonging to the same module tend to be anatomically colocalized [7,8], they cannot explain these effects entirely [94,95].”

      with the below paragraph:

      “Spatial biases may contribute to these findings, as nodes belonging to the same module tend to be anatomically colocalized [7,8]. Pantazatos et al. argued that these findings are entirely explained by spatial biases [94]. They showed that elevated CGE, as defined in the original Richiardi et al. study, falls monotonically as longer distance edges are removed. Moreover, they showed that 1,000 sets of randomly spaced modules all have significantly high CGE when using the same null distribution defined in the original Richiardi et al. analyses. Therefore, elevated CGE is not specifically related to functional segregation as defined by resting state functional networks, which is in direct contradiction to the main conclusion of the original Richiardi et al. study. Since randomly placed modules do not align (spatially) with any distributed pattern of functional segregation, the finding of elevated CGE may instead be attributed entirely to anatomical colocalization of the nodes within each module. In their rebuttal to [94], Richiardi et al. argue spatial biases cannot explain their findings entirely [95]. However, the authors do not offer an explanation for significantly high CGE observed for randomly spaced sets of modules, other than to note that nodes tend to be closer on average compared to when modules are defined by resting state fMRI. Future work is required to dissociate the effects of spatially proximity on relationships between CGE and spatially distributed functional networks.”

    1. A stunning thing that we forget, but the link here is not part of the author’s intent, but of the reader’s analysis. The majority of links in the memex are made by readers, not writers. On the world wide web of course, only an author gets to determine links.
  2. Aug 2018
    1. You might have seen the hashtag #BlackLivesMatter in the previous step. In this 6-minute video, #BlackTwitter after #Ferguson, we meet activists who were involved in the movement and learn about their own uses of Twitter as a platform of protest. Hashtags, when used like this, can be extremely complex in the way they represent ideas, communities and individuals.
  3. Jul 2018
    1. Then I used Gephi, another free data analysis tool, to visualize the data as an entity-relationship graph. The coloured circles—called Nodes—represent Twitter accounts, and the intersecting lines—known as Edges—refer to Follow/Follower connections between accounts. The accounts are grouped into colour-coded community clusters based on the Modularity algorithm, which detects tightly interconnected groups. The size of each node is based on the number of connections that account has with others in the network.
    2. Using the open-source NodeXL tool, I collected and imported a complete list of accounts tweeting that exact phrase into a spreadsheet. From that list, I also gathered and imported an extended community of Twitter users, comprised of the friends and followers of each account. It was going to be an interesting test: if the slurs against Nemtsov were just a minor case of rumour-spreading, they probably wouldn't be coming from more than a few dozen users.
  4. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. laudanum,

      Aw we have discussed before in class, there is a motif of addictive substances, like opium, alcohol and laudanum. It would be interesting to do a word collocation/concordance to in what context these substance arise. I would also be interesting in creating a network of the characters based on these substance to see which characters share the same bad habits!

    2. witnessed

      As we evaluate the many forms of "evidence" that the novel presents, we should ask ourselves how important or meaningful eyewitness accounts are in relation to testimonies, object clues, hearsay, and characters' inferences. An evidence network would allow us to visualize how information interacts and spreads, and modify our epistemological questions and detective work accordingly.

    3. Whether the letter which Rosanna had left to be given to him after her death did, or did not, contain the confession which Mr. Franklin had suspected her of trying to make to him in her life-time, it was impossible to say.

      Letters have been used throughout the text to add detail and action to the narrative. It would be interesting to create some kind of network connecting the senders and receivers of the letters and see which characters are the receivers and relayers of the information they provide. I would imagine Mr. Betteridge would be a major hub, but I think it would be interesting to see how they all connect and relate.

  5. Apr 2016
  6. Jan 2016