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12 MAY 2013

With Enough Data, the Numbers Speak for Themselves...

"Not a chance. The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big–data evangelists avoid taking a hard look at the weaknesses. Numbers can't speak for themselves, and data sets –– no matter their scale –– are still objects of human design. The tools of big–data science, such as the Apache Hadoop software framework, do not immunize us from skews, gaps, and faulty assumptions. Those factors are particularly significant when big data tries to reflect the social world we live in, yet we can often be fooled into thinking that the results are somehow more objective than human opinions. Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation."

(Kate Crawford, 12 May 2013, Foreign Policy)



Apache Hadoop • biasbig data • big-data science • blind spot • causal relationshipscausationcodecomputer utopianism • consumer spending • criminal actscyberspacedata abstractiondata analysisdata collection and analysisdataset • Foreign Policy (magazine) • globalisationhealthy habitsimplicit informationimplicit meaningInternetinternet utopianism • looking at the numbers • network ecologynetworked society • objects of human design • patterns of human behaviourpatterns of meaningquantified measurementreliability and validityscientific ideas • security intelligence • social world • terrorist acts • Twitterunderlying order • universal insights • universal methoduniversal rationality


Simon Perkins
16 MARCH 2013

Complex representations not simple quantified measurement

"Primarily because of its association with achievements in the physical sciences, quantified measurement seems a step toward enhanced precision. But, precision, as understood here, means more than reliability and validity; it also requires appropriately complex representation of the target construct. In phenomenological terms, precision refers to the distinctiveness that fosters reliability, the coherence that assures validity, and the richness that is appropriate to the targeted phenomenon. First, distinctiveness is the extent to which a phenomenon is discriminable from others. Judgments about distinctiveness require more than explicit (e.g., operational) definitions. They require the capacity to anticipate attributes that remain implicit in even the most explicitly conceived phenomenon and, on the basis of those implicit meanings, to consistently verify that phenomenon's presence or absence. Second, coherence is the extent to which judgments about the attribute structure of a particular phenomenon are congruent. Short of logical entailment but beyond associative contingency, judgments about coherence require consideration of both the explicit and implicit meanings of the attribute structure they describe. Third, richness is the extent to which judgments about a phenomenon capture its complexity and intricacy. Richness entails full differentiation of a phenomenon's attributes, identification of its attribute structure, and appreciation of its structural incongruities."

(Don Kuiken and David Miall, 2001)

[4] profiles and the ideal prototype. This numeric assessment of degree involves profiles of attributes rather than individual attributes. Although we appreciate the potential importance of the latter (see note 3), we have not attempted to address the analytic problems that arise from the combination of nominal and ordinal variables in estimates of profile similarity. It should be noted, however, that some available software facilitates the assessment of ordinal information during attribute identification (cf. KUCKARTZ 1995; WEITZMAN & MILES 1995). The possibility of coordinating ordinal and nominal attribute judgments deserves further consideration.

Kuiken, Don & Miall, David S. (2001). "Numerically Aided Phenomenology: Procedures for Investigating Categories of Experience." [68 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 2(1), Art. 15, http://nbn–– fqs0101153.


2001academic journalappropriately complex representation • associative contingency • coherencecomplexity • David Miall • differentiation • discriminable • distinctiveness • Don Kuiken • Eben Weitzman • explicit definitionsexplicit knowledgeexplicit meaningexplicit objectivesexplicitly definedForum Qualitative Social ResearchFQSimplicit informationimplicit meaning • implicitly • imprecision • intricacyinvestigative praxis • judgments • logical entailment • Matthew Miles • online journaloperational criteriaoperational definitionsphenomenologicalphenomenonphysical sciencesprecisionqualitative researchquantification of variablesquantified measurementreliabilityreliability and validityrich descriptions • richness • structural incongruities • target construct • targeted phenomenon • Udo Kuckartz • validity


Simon Perkins
14 FEBRUARY 2010

Get your recommendations in Spotify

"Another improvement has been rolled out to Spotify recommendations engine Spotibot. The service, which aims to help users find music they like, but just don't know yet, has added integration.

What that means is that instead of having to put in a song or band name you like, you can just get recommendations based on your recent listening instead. You just put in your account name, or the account of anyone else for that matter, and you can generate a list of 5 to 30 songs that you're going to love. Well, maybe. ...

This is just the 'recommendations' bit of at work. It also has 'your library', 'neighborhood' and 'loved tracks' functionality that could easily be added in the same way, so keep your eyes on Spotibot over the next month or so."




Simon Perkins

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