1,073 Matching Annotations
  1. Feb 2020
  2. Jan 2020
    1. What does it mean for a matrix UUU to be unitary? It’s easiest to answer this question algebraically, where it simply means that U†U=IU^\dagger U = IU†U=I, that is, the adjoint of UUU, denoted U†U^\daggerU†, times UUU, is equal to the identity matrix. That adjoint is, recall, the complex transpose of UUU:

      Starting to get a little bit more into linear algebra / complex numbers. I'd like to see this happen more gradually as I haven't used any of this since college.

    1.  ) +)

      Krippendorff, aquí en la bibliografía: Content analysis. An introduction to its methodology.

  3. Dec 2019
    1. “A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).”

      What is valence in music according to Spotify?

    1. Before each election, I have traditionally written up an analysis of the California ballot measures and send it to my friends. It's not always obvious what the "real" agenda is on each one, and even with clear purposes there are often competing interests at play. These writings are the result of my own analysis, which comes from a libertarian perspective, and I'm not knowingly affiliated with any party behind any ballot measure. I believe that mere lists of "vote yes" or "vote no" are not very helpful except for sheep: it's important to know why one is urged to vote in any given direction. I would rather you vote against my position because you had an opposing view than vote with my position because you flipped a coin.
  4. Nov 2019
    1. This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

      what is time series?

  5. Sep 2019
    1. Estimated economic benefit of data linkage

      the potential value from linking Census data to administrative data sets is only beginning to be realised and holds immense potential.(In other work for the Population Health Research Network, Lateral Economics concluded that data linkage generated over $16 for every dollar invested).

    2. Economic benefit of the Census.

      Our estimates suggest the benefits of running the Census easily outweigh its costs in the order of$6 of economic value for each $1 it costs. On this reckoning, the cost of the Census would have to rise to six times its current cost –to around $3 billion every five years –before it startedto become cost ineffective

  6. Aug 2019
    1. so that instead of predicting the time of event, we are predicting the probability that an event happens at a particular time .
  7. Jul 2019
    1. Children are going hungry too. Almost 14% of kids, or some 3.5 million in all, are estimated to live in poverty -- and that’s already down from a peak of more than 16% in 2012. To combat the problem, local governments around the country are opening thousands of cafeterias where children can eat for free.
    2. Given all of this good behavior, conservatives might expect that Japan’s poverty rate would be very low. But the opposite is true; Japan has a relatively high number of poor people for an advanced country.
    1. In practice, we found that it is not appropriate to use Aalen’s additive hazardsmodel for all datasets, because when we estimate cumulativeregression functionsB(t),they are restricted to the time interval where X (X has been defined in Chapter 3) is offull rank, that meansX0Xis invertible. Sometimes we found that X is not of full rank,which was not a problem with the Cox model.
    2. An overall conclusion is that the two models give different pieces of informationand should not be viewed as alternatives to each other, but ascomplementary methodsthat may be used together to give a fuller and more comprehensive understanding ofdata
    3. The effect ofthe covariates on survival is to act multiplicatively on some unknown baseline hazardrate, which makes it difficult to model covariate effects that change over time. Secondly,if covariates are deleted from a model or measured with a different level of precision, theproportional hazards assumption is no longer valid. These weaknesses in the Cox modelhave generated interest in alternative models. One such alternative model is Aalen’s(1989) additive model. This model assumes that covariates act in an additive manneron an unknown baseline hazard rate. The unknown risk coefficients are allowed to befunctions of time, so that the effect of a covariate may vary over time.
    1. Note that, three often used transformations can be specified using the argument fun: “log”: log transformation of the survivor function, “event”: plots cumulative events (f(y) = 1-y). It’s also known as the cumulative incidence, “cumhaz” plots the cumulative hazard function (f(y) = -log(y))
    2. Note that, the confidence limits are wide at the tail of the curves, making meaningful interpretations difficult. This can be explained by the fact that, in practice, there are usually patients who are lost to follow-up or alive at the end of follow-up. Thus, it may be sensible to shorten plots before the end of follow-up on the x-axis (Pocock et al, 2002).
    1. our sum of squares is 41.187941.187941.1879

      Just considering the Y, and not the X. Calculating the residuals from the average/mean Y.

    1. RF is now a standard to effectively analyze a large number of variables, of many different types, with no previous variable selection process. It is not parametric, and in particular for survival target it does not assume the proportional risks assumption.
    1. Thesurvival function gives,for every time,the probability of surviving(or not experiencing the event) up to that time.The hazard function gives the potential that the event will occur, per time unit, given that an individual has survived up to the specified time.
  8. May 2019
    1. Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

      Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.

  9. Mar 2019
    1. This is Bloom's taxonomy of cognitive objectives. I selected this page because it explains both the old and new versions of the taxonomy. When writing instructional objectives for adult learning and training, one should identify the level of learning in Blooms that is needed. This is not the most attractive presentation but it is one of the more thorough ones. rating 4/5

    1. Você consegue visualizar a saúde da sua aplicação?

      Ainda que aqui os tópicos da certificação não cubram exatamente esse assunto, monitorar a saúde de um sistema e suas aplicações é missão do profissional DevOps. Atente para os tópicos:

      701 Software Engineering 701.1 Modern Software Development (weight: 6)

      e

      705.2 Log Management and Analysis (weight: 4)

  10. Feb 2019
    1. set; if this is higher, the tree 2can be considered to fit the data less well

      To test the fit between data and more than one alternative tree, you can just do a bootstrap analysis, and map the results on a neighbour-net splits graph based on the same data.

      Note that the phangorn library includes functions to transfer information between trees/tree samples and trees and networks:<br/> Schliep K, Potts AJ, Morrison DA, Grimm GW. 2017. Intertwining phylogenetic trees and networks. Methods in Ecology and Evolution (DOI:10.1111/2041-210X.12760.)[http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12760/full] – the basic functions and script templates are provided in the associated vignette.

    1. especially at a time when many (perhaps most) computer technologies appear untethered to any philosophy besides the pursuit of maximum profit

      This is why I am here. As we have become more and more specialized, we have become less capable of understanding the consequences, good or ill, of new technologies. Looking back at foundational documents like this with a critical eye is a first step. We can't divorce science and technology from history, ethics and critical analysis without suffering the consequences. Looking back and understanding how we got here will provide clues in how to fix things. I am Geoff Cain - I started out life as a writer and English teacher and eventually went into elearning. I am VERY interested in projects like this because we need to stop being passive consumers of information. I want to help end the Era of the Guilty By-Stander: shared thought can lead to shared action. I will be blogging my experiences with this project at http://geoffcain.com

  11. Jan 2019
  12. www.at-the-intersection.com www.at-the-intersection.com
    1. Kind of the technical philosophy is everything that happens in the market is captured in the data and so any headline moves will be captured pretty much instantaneously or in a few minutes in the charts.
    2. Yeah, uh, I would say for reallocating, I'm, yeah. So I would say on Gemini I do Bitcoin, ethereum, and that's kind of like the longer term things.
    3. Uh, I definitely have some other, you know, mostly it's mostly I use ta very, very ta heavy. Um, I will, but I'll always keep the fundamentals in mind, especially for the medium to long term.
    4. I really try to focus on technicals cause I mean, yeah, the technicals is, is supposed to be representative, at least from an historical standpoint of the sentiment, right? Like if it's, if it's losing, if people are losing faith in it, then you'll probably see where did it go down? You'll see the price get affected by it. Um, and I tried to just trade on that. I try to minimize my sources all over the place.
    5. So depending on where you're trading, you could put more emphasis on where the other, when when you're doing fundamental analysis on a stock, there's a lot more information going into that, you know, potential company valuation. Um, whereas I would argue most cryptocurrencies heavily lack fundamentals at all.
    6. I personally try to trade based on technicals only. I'll read stuff for more general and for information. Um, but I guess the way I look at is like technical is this more short term? And fundamentals is more longterm.
    7. Uh, yeah, I'm in a few groups. There's a couple of the crypto focused, uh, the also have been just, I wouldn't say [inaudible], but have put more emphasis on, you know, since we're technical traders, there's a reason not to take advantage of, uh, the market opportunities and traditional as they pop up. So we've been focused mainly on just very few inverse etfs to short the s&p to short some major Chinese stocks, um, doing some stuff with, uh, oil, gas. And then there's some groups that I'm in that are specifically focused on just traditional, uh, that are broken up or categorized by what they're trading.
    1. This is because most authors usually did not report a plausible theoretical model for the structure of their observed variables, and there was often insufficient information for us to create our own plausible non-g models that could be compared with a theory of the existence of Spearman’s g in the data

      The EFA vs. CFA question was a stickler for one peer reviewer, and I can understand why. When measurement is based on strong theory, then I believe that CFA is preferable to EFA. But that was rarely the case in these datasets.

    2. the strongest first factor accounted for 86.3% of observed variable variance

      I suspect that this factor was so strong because it consisted of only four observed variables, and three of them were written measures of verbal content. All of the verbal cariables correlated r = .72 to .89. Even the "non-verbal" variable (numerical ability) correlates r = .72 to .81 with the other three variables (Rehna & Hanif, 2017, p. 25). Given these strong correlations, a very strong first factor is almost inevitable.

    3. The weakest first factor accounted for 18.3% of variance

      This factor may be weak because the sample consists of Sudanese gifted children, which may have restricted the range of correlations in the dataset.

    4. The mean sample size of the remaining data sets was 539.6 (SD = 1,574.5). The large standard deviation in relationship to the mean is indicative of the noticeably positively skewed distribution of sample sizes, a finding supported by the much smaller median of 170 and skewness value of 6.297. There were 16,559 females (33.1%), 25,431 males (48.6%), and 10,350 individuals whose gender was unreported (19.8%). The majority of samples—62 of 97 samples (63.9%)—consisted entirely or predominantly of individuals below 18. Most of the remaining samples contained entirely or predominantly adults (32 data sets, 33.0%), and the remaining 3 datasets (3.1%) had an unknown age range or an unknown mix of adults and children). The samples span nearly the entire range of life span development, from age 2 to elderly individuals.

      My colleague, Roberto Colom, stated in his blog (link below) that he would have discarded samples with fewer than 100 individuals. This is a legitimate analysis decision. See his other commentary (in Spanish) at https://robertocolom.wordpress.com/2018/06/01/la-universalidad-del-factor-general-de-inteligencia-g/

    1. Overall, recovery studies suggest that subcategories of the recovery process exist. However, different units of analysis (e.g., individual versus group) or different types of groups (e.g., based on ethnicity or social class) may experience the phases of recovery at differing rates. Thus, patterns, phrases or cycles of recovery are not linear.

      Strong statement on how the unit of analysis can influence disaster research beyond theoretical frameworks and the need to look at temporality differently.

  13. Dec 2018
    1. Ethnographic findings are not privileged, just particular: another country heard from. To regard them as anything more (or anything less) than that distorts both them and their implications, which are far profounder than mere primitivity, for social theory.

      This tension exists in HCI as well.

      Interpreted data vs empirical data and how each is systematically analyzed.

  14. Nov 2018
    1. On the Spectral Bias of Deep Neural Networks

      就是这么一句话:“we study deep networks using Fourier analysis.” 让我立马收藏此文,好好读读看!

      Paper Summary

    1. The transition is a keen one, I assure you, from a schoolmaster to a sailor, and requires a strong decoction of Seneca and the Stoics to enable you to grin and bear it. But even this wears off in time.

      This phrase is typical of Melville's sentence construction. Comparison is the mean and reason is the goal.

  15. Oct 2018
    1. ghosts

      These ghosts are representative of past lovers who are haunting her, which is especially disturbing to the speaker because she cannot remember them. Perhaps she cannot remember the past lovers because she was promiscuous rather than trying to find real love. I think Millay chose the word ghost because usually things that haunt you are things that you feel guilty about, and I think Millay feels guilty for her past behavior of being promiscuous.

    2. A little while, that in me sings no more.

      This line also contributes to the overall gloominess of the text because it does not indicate any sort of hope for the future or incline that something will change for the speaker, whether that be her getting her memories back or her companionship and and presumable happiness back. They are saying that they were only happy for a little while and will never be happy again.

    3. I have forgotten, and what arms have lain

      The speaker feels as though she should have deeper associations and more vivid memories of these past experiences.

    4. I cannot say what loves have come and gone,

      The speaker might not know what loves have come and gone; However, she obviously senses on a base level that she was once happy and that she no longer is. The pain in her heart is "quiet" but it is very much present and continuing to hurt the speaker.

    1. We want to make our model temporally-aware, as furtherinsights can be gathered by analyzing the temporal dy-namics of the user interactions.

      sounds exciting

  16. 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.”

  17. Aug 2018
    1. This text analysis that it contains words written in hebrew and deciphering of the first sentence of the text using hebrew translation seems to align with what this author is saying about the text being passed down through the family.

      She made recommendations to the priest, man of the house and me and people.

      [Source] (https://hyp.is/GB7sZKjvEeidoGeGo8L6jA/www.independent.co.uk/news/science/mysterious-manuscript-decoded-computer-scientists-ai-a8180951.html)

    1. Comments, questions, suggestions? Your feedback is welcome.

      Sukhwant Singh's analysis here seems to fit with a lot of other's partial analysis/observations such as multiple characters representing the same character, certain characters only appearing at the end of words etc. It seems quite compelling. The dates however, are a century too early although that does not necessarily dispel his theory that it is written in Landa Khojki.

    2. Many "words" differ by only one character and are found in each other's vicinity

      This might suggest the same thing as Tiltman's analysis in that a single character may take several forms.

    3. Tiltman treats f as a variant form of k and p as a variant form of t

      When learning that there were over 100 characters used in the manuscript my first thought was that perhaps variations of a character were used to represent the same character.

    4. Speaking generally, each character behaves as if it has its own place in an 'order of precedence' within words; some symbols such as o and y seem to be able to occupy two functionally different places.

      This is very interesting. It seems to suggest that each word may be scrambled based on the characters used.

    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.
  18. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. The smile passed away from Gabriel’s face. A dull anger began to gather again at the back of his mind and the dull fires of his lust began to glow angrily in his veins

      There seems to be a lot of sharp turns in the mood of the main characters in the story; not long ago Gabriel had been "trembling with delight at her sudden kiss", I wonder if we could see such a pattern of highs followed by significant lows by doing a sentiment analysis on the stories in Dubliners. On the other hand, Gabriel strongly reminds me of Mr. Hammond in Mansfield's The Stranger, as his solicitude, anxiety regarding women, and overprotected-ness of his wife is emphasized several times throughout this story, and in the end a dead man also drives the wedge between the couple.

    2. No memory of the past touched him, for his mind was full of a present joy.

      There's a quick (and almost absurd) change in mood where just in the previous paragraph he was sad when thinking about life; I'm guessing that there will be a fairly steep slope here if we do a sentiment analysis.

  19. Jul 2018
  20. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. Then she dried her eyes and went over to the looking-glass. She dipped the end of the towel in the water-jug and refreshed her eyes with the cool water. She looked at herself in profile and readjusted a hairpin above her ear. Then she went back to the bed again and sat at the foot. She regarded the pillows for a long time and the sight of them awakened in her mind secret, amiable memories. She rested the nape of her neck against the cool iron bed-rail and fell into a reverie. There was no longer any perturbation visible on her face.

      Polly's actions here could be analyzed by extracting the past tense verbs. She seemed to fall into a reverie of the future. Perhaps we could weigh her actions with regard to Mr.Doran's to penetrate what they genuinely expect from the affair.

    2. NORTH RICHMOND STREET, being blind, was a quiet street except at the hour when the Christian Brothers’ School set the boys free

      This first sentence already raises some questions. What does it mean for North Richmond Street to be "blind" (sightless?)? And why is the Christian Brothers' School characterized as a prison from which boys are "set free"? We could explore the second question further by creating concordances and collocations with words associated with freedom and captivity.

    3. peaceful and resigned

      James Joyce played around with adjectives for the corpse of the late Father. He described his face as 'very truculent, grey and massive, with black cavernous nostrils and circled by a scanty white fur' a few lines before, but here, he turned the whole picture around completely!

    4. We pleased ourselves with the spectacle of Dublin’s commerce—the barges signalled from far away by their curls of woolly smoke, the brown fishing fleet beyond Ringsend, the big white sailing-vessel which was being discharged on the opposite quay. Mahony said it would be right skit to run away to sea on one of those big ships and even I, looking at the high masts, saw, or imagined, the geography which had been scantily dosed to me at school gradually taking substance under my eyes. School and home seemed to recede from us and their influences upon us seemed to wane.

      The narrator's description of the commercial ships, and his fantasy of sailing away from Dublin, briefly suspend the narrative, creating a temporal and spatial expansiveness that pressures the story's geographic containment. It would be interesting to track and investigate the language of imagination and fantasy throughout Dubliners with a concordance and collocations.

    5. I wished to go in and look at him but I had not the courage to knock. I walked away slowly along the sunny side of the street, reading all the theatrical advertisements in the shopwindows as I went. I found it strange that neither I nor the day seemed in a mourning mood and I felt even annoyed at discovering in myself a sensation of freedom as if I had been freed from something by his death. I wondered at this for, as my uncle had said the night before, he had taught me a great deal. He had studied in the Irish college in Rome and he had taught me to pronounce Latin properly. He had told me stories about the catacombs and about Napoleon Bonaparte, and he had explained to me the meaning of the different ceremonies of the Mass and of the different vestments worn by the priest. Sometimes he had amused himself by putting difficult questions to me, asking me what one should do in certain circumstances or whether such and such sins were mortal or venial or only imperfections.

      Packed with mixed feelings of curiosity, fear, deferral, confusion, irritation, freedom, and reminiscing, this passage suggests that the narrator's relationship with the late Father Flynn is far more complicated (and perhaps troubling) than meets the eye. The language of this excerpt is ripe for close reading, but we could also explore it computationally by calculating pronoun frequencies (i.e., how often "he" and "him" appear compared to "I" and "me"), and by performing a sentiment analysis in a Jupyter notebook. It would be interesting to compare one's close readings with the results of a computer-generated sentiment analysis.

    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.
    1. Even Charlotte and the girls were too much for him to-night. They were too... too... But all his drowsing brain could think of was—too rich for him.

      I'm curious about what we would find by tracking the word "too" across the entire Mansfield short story corpus, mainly with word counts, word collocations, and n-grams. My hypothesis would be that the word "too" appears most frequently (perhaps exclusively) in stories with thematics of excess, both material and immaterial.

    2. Out came the thin, butter-yellow watch again, and for the twentieth—fiftieth—hundredth time he made the calculation.

      The watch is a recurring motif in this story. And time after time the 'thin', 'butter-yellow' aspects of the watch were underscored. And here Katherine demonstrated a peculiar use of the token '----' , maybe she used it in consistence with her other stories such as The Garden Party.

    3. shrewd grey

      The author devoted a lot of beautiful adjectives to delineate Mr.Hammond's glance: 'quick', 'eager', 'nervous', 'shrewd', and so on. Maybe fetching those words would facilitate our understanding of this character.

    4. “Dreadfully sweet!

      There is definitely a lot negative sentiment in this narrative. It would be interesting to do a sentiment analysis with this story and the Garden Party, which seems to have more a positive connotation. I would be interested in seeing which turns out to be the more negative of the two.

    5. There came a little rustle, a scurry, a hop.

      There have been some interesting verbs in the narrative thus far, especially this little cluster here. While the use of verbs has been frequent, the verbs them self have been some what gentle and not aggressive or assertive. I would be interested in performing a word frequency analysis to gather all the verbs, followed by a sentiment analysis to see if they are congruent with this theme of gentle submission / obedience which arises in the text.

    6. Meringues

      The recurrence of "meringues" until this point warrants further examination. In addition to tracing this motif and performing contextual analyses, we can use word collocations and concordances to better understand the physical and figurative resonances of this dessert. This reading could then feed into a broader analysis of all of the story's ingestible substances.

    7. How strange! She looked up at the pale sky, and all she thought was, “Yes, it was the most successful party.”

      There is no denying that Laura thinks a party with the dead is not a successful party. So I supposed that it is a verbal irony. There are also many ironies in The Moonstone. How can we track the irony through a computational method? I think this example of irony gives me an inspiration. There are some passive words which indicate the meaning that is ostensibly expressed in this paragraph, but "the most successful" are positive words. The strong contrast can be a typical symbol for the computer to recognize irony.

    8. The very smoke coming out of their chimneys was poverty-stricken. Little rags and shreds of smoke, so unlike the great silvery plumes that uncurled from the Sheridans’ chimneys. Washerwomen lived in the lane and sweeps and a cobbler, and a man whose house-front was studded all over with minute bird-cages. Children swarmed.

      A comparison of the smoke that came out of the chimneys brings about a pathetically stark contrast of the socioeconomic lives of people who lived in such proximity. Perhaps an extraction of the adjectives used to describe their chimney smokes could better demonstrate the differences.

    9. “My dear!” trilled Kitty Maitland

      The author implements zoomorphism, using the sound of birds to describe how the characters talk: Jose "cooed" liked a dove, Kitty Maitland "trilled" like a warbler, and "'Tuk-tuk-tuk,' clucked cook like an agitated hen." The author also uses a lot of adverbs to modify other verbs, such as "oily", "meaningly", "fondly", etc, I think we could run a parts of speech analysis on the contrast of ways of behaviour between characters from the Sheridan household and the village.

  21. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. beyond any reasonable doubt

      With this key phrase of legal discourse, Cuff's takes on the role of a prosecutor in a courtroom, shouldering the burden of proof in the case at hand. What is the relationship between detective work and legal argumentation? How does the novel's language put various characters on trial, not only before other characters, but also before the novel's jury of readers? A word collocation analysis of words and phrases with legal significance would help us to determine whether or not legal language shapes standards of evidence in The Moonstone.

    2. 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!

    3. Here, again, there is a motive under the surface; and, here again, I fancy that I can find it out.

      Once again, we encounter the language of detective work, which often involves the uncovering and probing of underlying motives. What distinguishes Betteredge's "detective fever" from Ezra Jennings's understanding of detection? We could operationalize this comparative question into a word collocation study of target words that are associated with detective work (e.g., "detective," "suspect," "motive," etc.).

    4. The chance of searching into the loss of the Moonstone, is the one chance of inquiry that Rachel herself has left me.”

      Throughout his narrative Blake repeatedly links the mention of Rachel to the mention of the Moonstone / Diamond. I would be interested in running a word colocation / frequency analysis to see how often this happens in Blake's narrative and throughout the rest of the text. It may also be worth while to do a sentiment analysis and see what the tone is for each mention based on which narrative it came occurred in.

    5. The picture presented of me, by my old friend Betteredge, at the time of my departure from England, is (as I think) a little overdrawn. He has, in his own quaint way, interpreted seriously one of his young mistress’s many satirical references to my foreign education; and has persuaded himself that he actually saw those French, German, and Italian sides to my character, which my lively cousin only professed to discover in jest, and which never had any real existence, except in our good Betteredge’s own brain. But, barring this drawback, I am bound to own that he has stated no more than the truth in representing me as wounded to the heart by Rachel’s treatment, and as leaving England in the first keenness of suffering caused by the bitterest disappointment of my life.

      Although the novel has already suggested that Betteredge is an unreliable narrator, Franklin Blake's explicit refutation of specific points in the First Period account corroborates the faultiness of Betteredge's perspective. Whom should we trust? Maybe a comparative sentiment analysis of Betteredge and Blake's accounts would allow us to test Blake's claim that Betteredge has "overdrawn" his narrative and interpreted passing references too "seriously."

    6. And what of that?–you may reply–the thing is done every day. Granted, my dear sir. But would you think of it quite as lightly as you do, if the thing was done (let us say) with your own sister?

      Mathew Bruff carefully anticipates the reader's objections, and tries to persuade him ("my dear sir") to reconsider his assessment of Godfrey Ablewhite. To better understand how and why The Moonstone's various narrators directly address readers, we could run a word collocation analysis and/or a sentiment analysis on each moment that features a narrator addressing a reader. Then, we would be informed enough to speculate about the extent to which such addresses prove effective.

    7. There had once been an occasion, under somewhat similar circumstances, when Miss Jane Ann Stamper had been taken by the two shoulders and turned out of a room. I waited, inspired by HER spirit, for a repetition of HER martyrdom

      Now that we have a woman narrator, I would be interested to performing a sentiment analysis and compare Miss Clack with Mr. Betteredge and the words they use to describe the female characters in the story in addition to the overall differences in word use and phrasing. It would also make for an interesting study about the author and his ability to create distinct voices for each character.

    8. 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.

    9. 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.

    10. Being restless and miserable, and having no particular room to go to, I took a turn on the terrace, and thought it over in peace and quietness by myself. It doesn’t much matter what my thoughts were. I felt wretchedly old, and worn out, and unfit for my place–and began to wonder, for the first time in my life, when it would please God to take me. With all this, I held firm, notwithstanding, to my belief in Miss Rachel. If Sergeant Cuff had been Solomon in all his glory, and had told me that my young lady had mixed herself up in a mean and guilty plot, I should have had but one answer for Solomon, wise as he was, “You don’t know her; and I do.”

      The twists and turns in the investigation had exhausted Mr.Betteredge, but hadn't worn out his faith in Miss Rachel. Despite his awfully miserable feeling at Miss Rachel's being suspected, his belief in Miss Rachel never wavered. It's interesting how Betteredge's acquaintance with the suspect varied his viewpoint. Was his belief blinded by his familiarity with Miss Rachel? Was it a "human infirmity" that Mr.Betteredge tended to protect Miss Rachel? It appears necessary to analyze Betteredge's attitudes towards Miss Rachel to evaluate his statement of Miss Rachel's innocence here.

    11. I began to feel a little uneasy. There was something in the way Penelope put it which silenced my superior sense. I called to mind, now my thoughts were directed that way, what had passed between Mr. Franklin and Rosanna overnight. She looked cut to the heart on that occasion; and now, as ill-luck would have it, she had been unavoidably stung again, poor soul, on the tender place. Sad! sad!–all the more sad because the girl had no reason to justify her, and no right to feel it.

      Betteredge's demonstrative narrative here appeals to me. All along the way, the old man had been high up above, examining Rosanna's affections towards Mr.Franklin from behind the veils. However, the quiet stolidity of Rosanna described by his daughter "silenced" his "superiority sense". Not only that Rosanna had been stung in the tenderest chamber of her heart, but also her lack of justification of her feelings, had aroused the melancholy sense. I would be intrigued to analyze the turn of Betteredge's feelings towards Rosanna, as revealed here for an instance. Additionally, the old man's stereotypes towards Rosanna might be linked to class prejudice, since she was a mere servant in the house.

    12. Penelope fired up instantly. “I’ve never been taught to tell lies Mr. Policeman!–and if father can stand there and hear me accused of falsehood and thieving, and my own bed-room shut against me, and my character taken away, which is all a poor girl has left, he’s not the good father I take him for!”

      Penelope has been a compelling and independent character throughout the narrative, from the ways in which she gives insight to the narrative itself , to this moment here. It would be interesting to extract her character descriptions and speech and compare it to the other female characters in the story to see how they are different / similar. I would also be interested in comparing her to the male characters. Would this be something the sentiment analysis could be used for?

  22. Jun 2018
  23. May 2018
    1. Over half of the respondents (54%) reported not purchasing a required textbook at least once (see Figure 2). Bivariate correlations revealed that these individuals were more likely to hold a student loan [r(307) = .16, p =.01] and be working more hours per week [r(254) = .13, p = .05].
    1. one hand was stretched out, seemingly to detain me,

      There is so much misunderstanding! It is far more likely the monster was reaching for his father creator. In a way, he was just born. Victor is all he knows.

    2. while a grin wrinkled his cheeks.

      The monster seems happy to see his creator. It's what Victor wanted. The creature is grateful for life. Yet, Victor isn't by any means pleased by this.

    3. A new species would bless me as its creator and source; many happy and excellent natures would owe their being to me.

      There it is, Victor wants to be a creator of new life. He wants to be a god. He does not seem to realize however everything that role would entail.

    4. Darkness had no effect upon my fancy, and a churchyard was to me merely the receptacle of bodies deprived of life, which, from being the seat of beauty and strength, had become food for the worm.

      Victor thinks very logically about everything and tries to take a common-sense approach to anything he seems to come across. Most people are irked by graveyards and dead bodies, but to Victor- it isn't anything more than that. What I find interesting though is his disbelief in the supernatural. He wants to reanimate the dead and create new life, how then, does he not believe in ANYTHING supernatural?

    5. Thus ended a day memorable to me; it decided my future destiny

      And this is where it all seems to go downhill for him. Does he look back at the day with remorse? Does he look back at it with a reminiscent air? It's hard to tell, but I think it might be both.

    6. creation.

      Creation is the very basis of religion. If you find a way to mimic creation, you become closer to a god.

    7. Such were the professor’s words—rather let me say such the words of the fate—enounced to destroy me.

      The professor seems to mimic Victor's intense obsession with the idea of altering nature and playing god. He may not want to actually do it, unlike Victor, but he may still be interested in how it would work.

    8. It was a strong effort of the spirit of good, but it was ineffectual. Destiny was too potent, and her immutable laws had decreed my utter and terrible destruction.

      Destiny and Nature fight within Victor's mind over what's "right". Destiny is encouraging Victor to continue on to his pursuits of reanimating the dead as she wants to destroy Victor. Nature wants Victor to leave her alone, let her preside over things the way they are supposed to be. Though Nature is correct and following this path will ultimately lead Victor to safety- Destiny is far more alluring and attractive, fooling Victor into the terrible trap she has laid.

    9. Wealth was an inferior object, but what glory would attend the discovery if I could banish disease from the human frame and render man invulnerable to any but a violent death!

      He claims to be in awe of those who "know" Nature and all of her secrets and implies that he wants to be like them. Yet, here he seems to want to twist and defile nature rather than know her for what she really is. He wants to change the most universal truth about nature (death), as if trying to fix it rather than trusting Nature to do her job.

    10. I was their plaything and their idol, and something better—their child, the innocent and helpless creature bestowed on them

      His parents loved their "creation" with everything that they had. They saw this "innocent and helpless creature" and gave him everything he could need and more. Victor had nothing but remorse for his own "innocent and helpless creature". In a way, the creation is Victor's own child. Victors' expression of love, affection, and passion differ greatly from that of his parents.

    11. He strove to shelter her, as a fair exotic is sheltered by the gardener, from every rougher wind and to surround her with all that could tend to excite pleasurable emotion in her soft and benevolent mind.

      In contrast, Victor practically threw his creation out to face the harsh winds of the outside world (literally and metaphorically). His creation needed understanding and shelter, but instead, he tried to deny his very existence and throw him out.

    12. he will be like a celestial spirit

      In this novel, Dr. Frankenstein tries to play God. Though he fails himself in his own expectations of what that role might be, he doesn't seem to disappoint Walton who sees something otherworldly in him.

    13. Even broken in spirit as he is, no one can feel more deeply than he does the beauties of nature. The starry sky, the sea, and every sight afforded by these wonderful regions seem still to have the power of elevating his soul from earth.

      This is a man who has seen the beauty of nature, yet twisted it and made it into his own perverse creation. He cares so deeply for nature, because he knows how easily it can all go wrong. However, I feel he may fear nature more than actually care for it.

    14. This appearance excited our unqualified wonder.

      Their reaction to seeing the "monster" contrasts to the masses reactions when they come across him. Could it be that they were so desperate for any hint of civilization? Or maybe they seemed to value different qualities than those that society seems to value.

    15. You may deem me romantic, my dear sister, but I bitterly feel the want of a friend.

      Could this be foreshadowing of the goal of Frankenstein's "monster" later in the novel? When the monster is created, Frankenstein immediately rejects him. Despite educating himself on how to communicate with others, the monster is ignored and alienated everywhere. Walton's experience seems to be a mirror of what is yet to come in the novel. I believe this also raises a large question in the novel: if we can sympathize and feel pity for Walton- why can't we do the same for the "monster"?

    16. And now, dear Margaret, do I not deserve to accomplish some great purpose? My life might have been passed in ease and luxury, but I preferred glory to every enticement that wealth placed in my path.

      Though wealth can sometimes serve as a great motivator to those who don't already have it, it doesn't seem to do much for those who already have it. They are already living comfortable. They are far past the line of survival. So what then do they have to achieve? The meaning of their lives. That is the ultimate goal anyone could reach. In Maslow's Hierarchy of Needs, he specifies the order in which humans can feel accepted accomplished. Those who have wealth have already attained the bottom levels of physiological and safety, often times they have love and belonging. That means the next things they need to attain in order to feel accomplished would be esteem and self-actualization. I believe that's whats happening here.

    1. hi there check this link on SAS training and tutorial with the detailed explanation on the Base Training of that along with real time Examples and Projects practices

      https://www.youtube.com/watch?v=IOxaKq4lB-0

    2. hi there check on the SAS Training and Tutorial with better analysis On the Data and forecasting methods for better implication on Business analytics

      https://www.youtube.com/watch?v=1QPRhVGCTRE

    3. hi there please check on the Recent Updated SAS Training and Tutorial Course which can explain about the SAS and its integration with the R as well so please go through the Link:-

      https://www.youtube.com/watch?v=IOxaKq4lB-0

  24. Apr 2018
    1. sqlite> .mode column sqlite> .headers on

      At the start of your session,these will format your sqlite3 output so it is clearer, and add columns headers.

    2. pandas is a Python module used for manipulation and analysis of tabular data.

      Introduction to Pandas

      Pandas is used to manipulation and analysis of tabular data

    3. pandas official documentation

      pandas reference and tutorials include:full docs, 10minutes to pandas, blog tutorials

  25. Mar 2018
  26. Feb 2018
    1. Máire Ní Mhongáin

      As Ciarán Ó Con Cheanainn writes in Leabhar Mór na nAmhrán, the oldest written version of this song dates to 1814, and is found in MS Egerton 117 in the British Library. Oral lore in Conneamara has it that Máire Ní Mhongáin’s three sons joined the British Army, and that Peadar deserted soon after joining, and emigrated to America. It seems probable that their involvement was in the French Revolutionary Wars or the Napoleonic Wars, the major conflicts fought by the British Army in the final decade of the eighteenth century and the first decade of the nineteenth respectively.

      Máire Ní Mhongáin seems to have resonated among Irish emigrant communities in the United States. My evidence for this is that Micheál Ó Gallchobhair of Erris, County Mayo, collected songs from Erris emigrants living in Chicago in the 1930s, over a century after the occasion of ‘Amhrán Mháire Ní Mhongáin’s’ composition. It features in his collection, which you access via the following link: http://www.jstor.org.ucc.idm.oclc.org/stable/20642542?seq=2#page_scan_tab_contents

      The virulent cursing of departed sons by the mother, named Máre, produces the effect of striking g contrasts with John Millington Synge’s bereaves mother, Old Maurya, in Riders to the Sea.

      My Irish Studies blog features an in-depth account of typical features of the caoineadh genre to which Amhrán Mháire Ní Mhongáin belongs. You can access it via the following link: johnwoodssirishstudies.wordpress.com/2018/01/03/carraig-aonair-an-eighteenth-century-west-cork-poem/

    1. Bean an tSeanduine - Sean Nós 2

      ‘Bean an tSeanduine’ features all of the conventions of the malmariée genre we have previously encountered in ‘An Seanduine Cam’. Also, it is a good example of the speaker blaming her parents for her plight, which is another regular feature of this song type.

      As well as being one of the finest examples of the genre, it is perhaps the most well-known and commonly sung, owing in large part to the simplicity and catchiness of its monosyllable end-rhymes.

      As well as Ó Tuama, Meidhbhín Ní Úrdail has written about the common features of the chanson de la malmariée. Her article ‘The Representation of the Feminine: Some evidence from Irish language sources’ in Eighteenth-Century Ireland/Iris an Dá Chultúr is a rich source of information on the topic. In ‘Bean an tSeanduine’, we have a fine example of what Ní Úrdail calls the description of ‘the plight of a beautiful young woman, trapped in an unhappy marriage to an impotent elderly spouse who is ignorant of her mental and physical frustration’. However, when we consider the particular humour of this song, we can identify how it serves to empower the female speaker.

      ‘Bean an tSeanduine’ differs from ‘An Seanduine Cam’ in that there is no third-person narrator. Like ‘An Seanduine Cam’, the humour of the song relies on a ridiculing of the old man, although here the young woman herself is his detractor. Each of his brags meet a witty riposte. When he claims wealth, she calls him a miser, and when he wonders what would become of his if he died during the night, she jokes that death is an immanent danger. When mockery of this kind is voiced by the female speaker, it serves to empower her, and inspire in the listener a sense of sympathy and respect.

    1. An Seanduine Cam - Corn Uí Riada 2016

      The song’s first two verses are spoken by a third-person narrator. In its humorous exaggeration, the first verse caricatures recognized conventions of arranged marriage. This narrative consciously situates itself in a genre whose familiarity to the listener is a necessary part of the humour. It addresses the economic incentives which were the major precipitating factors of marriage arrangements in rural Ireland during the eighteenth century. It also invokes the misery which such marriages often visited upon young women.

      In his essay ‘Love in Irish Folksong’, Seán Ó Tuama identifies among typical features of the malmariée genre that ‘a young woman speaks (in the first person) of her anguish,’ that ‘the description of the husband can be unbelievably grotesque and ribald: he is humped, crippled; he coughs, grunts, whines at night; most of all, he is cold as lead, important, and completely fails to satisfy her desires’, and that ‘she discloses that she is going to leave him for a young man’ (149). ‘An Seanduine Cam’ provides clear examples of all of these traits.

      Moreover, because these tendencies find expression in a debate form, and are redoubled in response to the unfeeling man, the resistant character of the put-upon young woman is strongly emphasized.

  27. Jan 2018
  28. Dec 2017
  29. Nov 2017
    1. This took place on March 10, 1977, at the home of actor Jack Nicholson in the Mulholland area of Los Angeles.

      Could this be why Kubrick casted Nicholson for The Shining? See Rob Ager's analysis of sexual abuse themes in The Shining.

    1. novel method developed within the MAQC-III project utilizing the expression distributions, corrected for noise and batch effects, and assisted by random resampling, to compute DEG scores related to the Wilcoxon U test (Magic, see Additional file 1: Supplementary Note 2)
    1. MOSAiCS implements a model-based approach where the background distribution for unbound regions take into account systematic biases such as mappability and GC content and the peak regions are described with a two component Negative Binomial mixture model
  30. Oct 2017
    1. afocusontheanticipatoryandthefuturesuchthatmoreemphasisisgiventopredicting,intervening,andmanagingconsequencesratherthanunderstandingcauses;andthemoreeasyandsuccessfuladoptionandadaptionofdatatodifferentfieldswithlittlerisk

    Tags

    Annotators

    1. applying networkand content analyses

      I came across this article while doing research for last week’s blog. I know this is not a straight forward SNA article, but I found it very interesting since it is a combination of SNA and content analysis. Considering this week’s readings on different data collection method, I found their approach of collecting data from Twitter very unique. In this context, content analysis refers to analyzing tweets and their content. Recently, content analysis is being used in various fields. Even social researchers are taking this opportunity of exploring already existing data. Do you think you can use the combination of both SNA and content analysis in your own research field?

  31. Sep 2017
    1. ocial networks is relational data

      This is different than traditional social science which focuses on independent and dependent variables. For SNA, all data is related to all other data; no variables are mutually exclusive. This is why SNA is fundamentally descriptive.

    1. The projection score - an evaluation criterion for variable subset selection in PCA visualization

      "variable" typically means gene or locus in the context of biological data.

    1. While technology has loomed large in these accounts, several scholars argue that this shift toward participation extends important possibilities for posi-tively influencing daily life (Jenkins, 1992, 2006; Shirky, 2010). Others question the ability for a universal “participation” within new media cultures, suggesting people require adequate social and psychological resources, including time, for engagement (Irani, 2015; Turner, 2009).

    Tags

    Annotators

    1. Organizations such as Code for America (CfA) rallied support by positioning civic hacking as a mode of direct partici-pation in improving structures of governance. However, critics objected to the involve-ment of corporations in civic hacking as well as their dubious political alignment and non-grassroots origins. Critical historian Evgeny Morozov (2013a) suggested that “civic hacker” is an apolitical category imposed by ideologies of “scientism” emanating from Silicon Valley. Tom Slee (2012) similarly described the open data movement as co-opted and neoliberalist.
  32. Jul 2017
  33. May 2017
    1. ($20*3)-($20*3*.1) = $54

      10% of $2000(cost of camer) * 3days = Rental Price

      Rental Price - Commission = Rental Made This guy totally forgot taxes here.... :)

      54$ for 3 days 365 days a year about 50 % usage so roughly 180 days. $54 for 3 days $? for 180 days = $3240 about 740$ profit per year for a $2000 investment if he's 50% utilized over the year.

      Camera's Man this guy needed to crunch some more numbers. Camera's have compatibility issues....

    1. The reason this was possible in my view was that the Prophet understood that his power had no limits, since the source of it was the uniquely omnipotent divine source of all power: Allah.

      A great idea! When one has unlimited power, there is no insecurity or fear -- only solemn and calm assurety of victory. Thus morals are always preserved, and one never loses temper.

  34. Apr 2017
    1. En un lugar de la Mancha, de cuyo nombre no quiero acordarme,

      The specificity of place does not matter from the start. This lends a mythica quality to the narrative,

    1. I'm the developer of pyGeno. Here's a little script that does just that for the Gene TPST2, by using segment trees

      recipe for merging transcripts of a gene into a single compound transcript

  35. Mar 2017
    1. The Google Analytics Setup I Use on Every Site I Build : 글쓴이가 수없이 웹사이트에 GA를 설정해 보면서 얻는 경험으로 GA 스크립트의 로딩 자체를 최적화하는 방법과 커스텀 디멘션을 사용해서 유용한 추적설정을 한 내용을 모두 설명하고 있다. 설정 방법과 어떻게 보고 참고할 수 있는지까지 잘 나와 있어서 웬만한 사이트는 이 글의 내용을 그대로 따라 해도 될 정도고 여기 나온 내용을 바탕으로 사이트에 필요한 추적을 추가하기에도 좋다. 이 사람은 GA를 엄청나게 연구했는지 비슷한 작업을 매번 하다가 결국 autotrack 플러그인을 개발했고(이 플러그인만 사용해도 엄청 편하다) 이 글에서 설명한 코드를 참고할 수 있도록 analytics.js boilerplate를 만들어서 공유하고 있다.(영어)

      GA study

  36. Feb 2017
    1. Again I say, this is harmful,\ since the invention of arguments is by nature prior 10 the judgment of their validity,

      This is crucial for me. And it harkens back to Lanham as well.

  37. Jan 2017
    1. I started using Google Maps because of the ease of dragging the map. But Google Maps didn't require me to sign-up or join as a member.

      Google Maps certainly wasn't the first GPS Map app (mobile or web app) there were multiple GPS apps before it and that came after it. However the other strides that Google made, mostly in their accuracy and in their satellite imagery, they eventually became the de facto standard, with hundreds and hundreds of apps using their API. It makes you wonder if there is even a point in creating an equal competitor to Google Maps. There are other apps, like Waze, that provided different functionality to the GPS function (road blocks, police traps, etc.) and ironically enough they got acquired by Google. Mapquest (which has the second highest marketshare) I would say is an old relic, and Apple Maps in functionality doesn't do anything different. It makes me wonder if some companies have designed their products for the "Long Wow" so well that they've effectively created enough barriers of entry so that they have a de facto monopolization of the market

    1. Normal Stress = Normal force / area Normal Force decreases from finite value to zero while area increases to infinite. Shear stress+ Shear Force/area Shear force increases from 0 to finite value while area increases faster to infinity.

  38. Dec 2016
    1. evidence about obtaining higher productivity by using Agile methods

      If higher productivity came from including stakeholders in the frequent development releases, running a complementary scrum team on UX analysis should lead to improvement in quality.

  39. Oct 2016
    1. blast

      BLAST finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.

    1. One of the distinct pleasures in Pixar’s films is the pleasure of seeing the deepest of human struggles, timeless philosophical questions projected in and through remote forms of representation.
    2. Pixar’s Toy Story (Dir. John Lasseter, 1995) and WALL-E (Dir. Andrew Stanton, 2008) are “innocent” animated film about ob-jects, their value as cinema lies in their ability to complexly address human—and sometimes wholly adult—fears about meaninglessness, apocalypse, and oblivion.
    3. riveting mounds of metal dramatically revealed through the birds-eye shots that rival the buildings in height—are actually mile-high piles of trash.

      this is showing human waste and what it has done to Earth

    4. In WALL-E, the humans have converted themselves into floating objects—mounds of flesh laden with too many conveniences to move for them-selves.

      humans become lazy and complacent allowing things to be done for them

    5. Toy Story 3 (2010) be-gins with Andy’s toys playing pretend. The setting is the Old West. Sheriff Woody fights the Potato Heads-cum-bandits to rescue a moving train full of orphan trolls. The Potato Heads escape the train in Barbie’s dream car. But the train is headed toward the precipice at the end of a dynamited trestle. Despite Woody’s efforts, the train plunges off the edge. We hear it thud below and see smoke rise from the chasm. Cow-girl Jessie’s jaw drops in utter disbelief and disappointment. For several long seconds as the energy of the action se-quence diffuses, we believe that Woody has failed. Then Buzz lifts the train out of the hole. But the threat is not, as we had imagined, contained but is instead reanimated and escalated. The toys are assailed by a mushroom cloud of “death by monkeys” and finally an unnamed threat behind a red button in Evil Dr. Porkchop’s dirigible.
    6. Jessie perhaps best ar-ticulates the pleasure of being a toy, an object used for the innocent pleasure of others: “Even though you’re not mov-ing, you feel alive because that’s how they see you.” With this statement Jessie highlights with a disarming frankness the philosophy of being the animated toy. She reveals how remote, ocular con-trol paradoxically creates a kind of inti-macy and movement that is enviable—revealing self to self through shared, pretend movement.

      Claim. Evidence (Quote). Analysis. This is an excellent model for how to work with primary source material in your arguments.

    7. Perhaps more persuasive even than these final lines, are the final shots of the film in which the sparkling out-fits of the sequined trio of Barbie dolls mix with the starry wall-paper of the room, suggesting the space-like infinity to which Woody refers.

      Note the scene description and analysis here.

    1. Since Amy becomes a mirror to Sheldon, she must be intelligent, but her showings of this intelligence can be awkward.

      Is a female being intelligent not normal and make people uncomfortable?

    2. According to Sherri Inness, “Lisa Simpson of The Simpsons is one of the most influential smart female characters on television, but she is not depicted entirely posi-tively, being too studious and serious when compared with her brother Bart”

      being smart means you can't be 'normal' or 'relaxed'?

    3. For Bernadette, the roles of caretaker and mother offer little to no appeal.
    4. Sheldon remains awkward and unclear in its trajectory. Amy desires a more traditional relationship than what Sheldon currently offers, but Sheldon remains avoidant or clueless about those conventions
    5. and even families

      all independent women want families?

    6. the classic plot situa-tion of the boyfriend trying to find a way to make up his poor behavior to his girl

      lost me here. became very picky about any situation portrayed. Seems like it's saying woman shouldn't get their feelings hurt because that stereotypical and woman are better than that now. Can be vice versa too.

    7. t the end of season 4, Berna-dette announces that she successfully defended her dissertation and earned her Ph.D., and she then announces being recruited by a pharmaceutical company and getting paid a “buttload” of money. This announcement becomes an oppor-tunity to deride Howard (yet again) for his lack of Ph.D. and his less-significant career.

      is that a bad thing? '(yet again)' making it out like she's a horrible person for being something better than her significant other.

    8. She visits Leonard’s lab to learn more about his experiment

      woman should be more interested in men's research and achievements.

    9. Bernadette misses a key event because she remains under quarantine for possible yellow fe-ver because of drinking out of contami-nated petri dishes.

      'typical' woman mistake and the consequences from being so careless.

    10. Children’s programming further features scientists, such as Bill Nye The Science Guy. Steinke et. al ex-amined middle-schoolers’ responses to the scientists in these and other shows using traits such as “intelligence, domi-nance, alone, and respected”

      This falls under analysis because it shows stereotypes in tv. The show Bill Nye the science guy is a great example of stereotypical characters; he is depicted in the show as being alone, dominant, respected, intelligent, and awkward. This is frustrating because this is not the case for all scientist.

    11. This reinforcing of dominant values, however, becomes more compli-cated with The Big Bang Theory through representations of Bernadette and Amy. In particular, these nuances appear through their discussions of their areas of expertise and their careers, their per-formances of their work, and their exter-nal recognitions for their achievements.
    12. Roles for female characters in early sitcoms were domestic ones, such as housekeeper and child care-taker as in I Love Lucy (1951–1957), The Brady Bunch (1969–1974), and Leave It to Beaver (1957–1963).

      are these older expectations still something people expect from modern shows? since it's so normal and what an average show should include maybe

    13. The Big Bang Theoryseems progressive in that it represents female scientists alongside male scien-tists in ways that value their intelligence and professional achievements.

      counter argument for positive representation, at least more than most shows.

    14. These main male charac-ters appear in both work and domestic settings throughout the show. The three main female characters do appear in the work situations at different times, but their locations remain primarily in the domestic ones.

      general over view of where the woman are portrayed. Not taken into account that they also work and are scientists as much as the men.

    15. Children’s programming further features scientists, such as Bill Nye The Science Guy. Steinke et. al ex-amined middle-schoolers’ responses to the scientists in these and other shows using traits such as “intelligence, domi-nance, alone, and respected” (172) and found the overall response favorable.

      Do audiences prefer male presence for these types of roles?

    1. Trump had less “fleeting interjections” this debate than the first round, and slightly more interruptions. Clinton had slightly less fleeting interjections, and the same amount of interruptions. The moderators, however, were much more active in this debate than in the first. They interjected (usually to let the candidate know they were running out of time) 41 times throughout the 90 minute debate.
  40. Sep 2016
    1. The queue of electronic hands could take so long to get through that some students abandoned hope and lowered their hands while others got into the habit of raising their hand pre-emptively just so they’d have a spot in line if an idea came into their head later on.