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Which clippings match 'Patterns Of Meaning' keyword pg.1 of 1
20 MARCH 2014

Information visualisation through the analysis of image sources

Lev Manovich speaking about his work on the Selfiecity.net project (part 1) at the 'Visualized' conference in New York, 6-7 February 2014.

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CONTRIBUTOR

Simon Perkins
19 NOVEMBER 2013

TechNyou: Critical Thinking

"The resource covers basic logic and faulty arguments, developing student's critical thinking skills. Suitable for year 8–10, focused on science issues, the module can be adapted to suit classroom plans."

"TechNyou was established to meet a growing community need for balanced and factual information on emerging technologies. We are funded by the Australian Government Department of Industry, Innovation, Science, Research and Tertiary Education (DIISRTE). We operate in partnership with the University of Melbourne, where our office is based."

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TAGS

2011animated presentationAustralian Government • betting system • biasBlaise PascalBridge8 • broken logic • causal modes of comprehensioncausation • certainty • coincidenceconfidenceconsequences • counter-argument • critical thinkingdeceptiondecision makingDepartment of Industry Innovation Science Research and Tertiary Education (DIISRTE) • does not follow • emerging technologiesevidence-based argumentexpert advice • factual information • fallacious arguments • fallacy • false dilemma • faulty arguments • formal fallacy • forms of logic • gamblers fallacy • gamblinggullibility • head scratching questions • human behaviour • identify patterns • inference • informal fallacy • irrefutable data • James Hutson • logical argument • logical fallacylogical rationalitylogical rules of inferencelogical structurelogical-analytical paradigm • logically impossible • logically true • mathematical conceptsmathematical patternmathematicsmental tricksMike Mcraemisleadingmisunderstandingnon sequituropinionoversimplificationpatternspatterns of meaning • Pierre de Fermat • play the ball not the player • precautionary principle • precautionary tale • predictions • premise • probabilistic outcomes • probability • public informationreckon • repeated observations • risk • rules of logic • science issuessensemaking • straw-man arguments • TechNyou • tertiary education • theoriesthinking skillstrustunethical behaviourUniversity of Melbourne

CONTRIBUTOR

Liam Birtles
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)

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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

CONTRIBUTOR

Simon Perkins
31 MARCH 2013

Qualitative research primarily is inductive in its procedures

"qualitative inquiry is inductive and often iterative in that the evaluator may go through repeated cycles of data collection and analysis to generate hypotheses inductively from the data. These hypotheses, in turn, need to be tested by further data collection and analysis. The researcher starts with a broad research question, such as 'What effects will information systems engendered by reforms in the UK's National Health Service have on relative power and status among clinical and administrative staff in a teaching hospital?' [48].The researcher narrows the study by continually posing increasingly specific questions and attempting to answer them through data already collected and through new data collected for that purpose. These questions cannot all be anticipated in advance. As the evaluator starts to see patterns, or discovers behavior that seems difficult to understand, new questions arise. The process is one of generating hypotheses and explanations from the data, testing them, and modifying them accordingly. New hypotheses may require new data, and, consequently, potential changes in the research design."

(Bonnie Kaplan and Joseph A. Maxwell, p.38, 2005)

Kaplan, B. and J. Maxwell (2005). Qualitative Research Methods for Evaluating Computer Information Systems. Evaluating the Organizational Impact of Healthcare Information Systems. J. Anderson and C. Aydin. New York, Springer: 30–55.

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Bonnie Kaplandata analysisdata collectiondata collection and analysis • generating explanations • generating hypotheses • hypothesishypothesis testinginductive enquiryinductive proceduresinductive reasoningiterative cycleJoseph Maxwellpatterns of meaning • qualitative enquiry • qualitative researchresearch designresearch questionresearcher • specific questions

CONTRIBUTOR

Simon Perkins
20 DECEMBER 2012

How to design your research project

"What are your beliefs about how valid knowledge can be obtained? This will influence your approach to your research. If you are a positivist, for example, (who believes that valid knowledge can be obtained through a scientific approach), you are likely to choose a quantitative research method that begins with a theory and tests that theory. If you favour the social constructivist view that meaning is subjective, gained through interactions with others, you would be more likely to choose qualitative research methods that explores themes. Qualitative research is about generating theory and finding patterns of meaning."

(Centre for Academic Development and Quality, Nottingham Trent University)

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Abbas Tashakkori • Anthony Onwuegbuzie • audiencebeliefs • Centre for Academic Development and Quality • data collection • epistemological approach • epistemological beliefs • epistemologyethical considerationsethical issues • existing theory • experimental designs • generating theory • interactions with others • John Creswell • Journal of Mixed Methods Research • Judith Bell • Mark Weinstein • Martyn Denscombe • Matt Henn • meaning is subjective • mixed methods • mixed methods research • new knowledge • new research methods • new theory • Nick Foard • non-experimental design • patterns of meaningpositivistqualitative research • quantitative research methods • research • research aims • research approachresearch contributionresearch designresearch disseminationresearch methodologyresearch projectresearch questions • research theory • scientific approach • social constructivistsocial sciencetriangulationvalid knowledge

CONTRIBUTOR

Simon Perkins
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