20 Matching Annotations
  1. Jul 2021
    1. Claudia: How long did you live in the States?Yosell: Let's see, about 24 years. Out here in Mexico, I've probably been here for like a year and a half. Just barely, I guess.Claudia: What was it like coming back to Mexico? You said you made the decision on your own?Yosell: Yeah, I mean, I already did know about it just a little bit, so it wasn't too bad. It was just basically like Los Angeles, it's the same thing, really. Just the differences, the corruption out here, and how people treat you. I would probably walk down the street, and I would always get a dirty look or something. I'd always get checked by the cops here, that's a constant thing for me.

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  2. Oct 2020
  3. Sep 2020
  4. Jul 2020
    1. ruby-prof supports excluding specific methods and threads from profiling results. This is useful for reducing connectivity in the call graph, making it easier to identify the source of performance problems when using a graph printer. For example, consider Integer#times: it's hardly ever useful to know how much time is spent in the method itself. We are more interested in how much the passed in block contributes to the time spent in the method which contains the Integer#times call. The effect on collected metrics are identical to eliminating methods from the profiling result in a post process step.
  5. May 2020
    1. If you profile your users, you have to tell them. Therefore, you must pick the relevant clause from the privacy policy generator.
    2. In case you’re implementing any ADM process, you have to tell your users.
    3. If you’re selling products and keep record of users’ choices for marketing purposes, dividing them into meaningful categories, such as by age, gender, geographical origin etc., you’re profiling them.
  6. Jan 2019
  7. Oct 2017
    1. ‘themorepredictableresultwouldbeagradualdesertificationoftheculturallifeofindividualsnolongerabletoencounterwhatisunusual,unexpected,andsurprising.’[61]Ratherthanindividualizedbubbles,sharingsegregatessocialnetworkusersintoculturalbubblesofpreferences,products,andknowledge
    2. platformssuchasGoogleandFacebookthatoperatelike‘predictionengines’by‘constantlycreatingandrefiningatheoryofwhoyouareandwhatyou’lldoandwantnext’basedonwhatyouhavedoneandwantedbefore

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  8. Sep 2017
    1. In each case data was framed as repressive of notions of civil society or enforcing an impoverished or constrictive notion of citizenship. The perspectives of Tufekci and Cheney-Lippold provide valuable insight into how algorithms and data are powerful shapers of modern life. Yet, they leave little room for a different form of algorithmic citizenship that might emerge where indi-viduals desire to reform technology and data-driven processes. As Couldry and Powell (2014) note, models of algorithmic power (Beer, 2009; Lash, 2007) tend to downplay questions of individual agency. They suggest a need to “highlight not just the risks of creating and sharing data, but the opportunities as well” (p. 5). We should be attentive to moments where meaningful change can occur, even if those changes are fraught with forces of neoliberalism and tinged with technocracy.

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  9. Jan 2016