Archived Tweets / Research Data
Codes & Themes w/wo Theoretical Memos
(derived through a process of inductive, qualitative, data analysis)
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@KhadijahBoster [name pseudonymized] [ontology] [06]: @ZhangZhangZhang @Madamecurves @milouwahwah @DCORVEC @CallGate74 @azureen_bis @radioclassique Le message ne s'adressait pas à vous mais à cette «anomalie maladive de la nature» qu'est @Madamecurves évoluant malsainement sur cette mode #woke désorientant les consciences. Sans même connaître l'effet dévastateur à petit feu sur la jeunesse, des sociétés et ses traditions [URL redacted]
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#PhenomnDeprivation
#MetaphorVersusSimpleComplexAnswers
#ReasoningFallacyOfIncompleteEvidenceCherryPicking
#MetaphorVersusFailureSuccess
#MetaphorVersusSideBySideDiptych
Supplementary #ReasoningFallacyBiasSurvivorship [Wikipedia] "Survivorship bias occurs when a social or political process causes many units to drop out of the sample, while those which survive and enter the sample exhibit peculiar features, consistent with their survival, that makes them non-representative of the larger population. Left uncorrected, survivorship bias can lead to biased conclusions. Therefore, standard practice is faulty. Standard practice is to estimate a conditional probability function, such as the probability of autocratic survival conditional on access to resource rents, on all units in a cross-sectional and longitudinal data set that spans the globe. Using all of the relevant data would seem to assure most scholars that they are not inducing bias through selecting a biased set of cases; but with survivorship bias, the bias is built directly into the data set." Smith, B., & Waldner, D. (2019). 'Borders, sovereignty, and sample selection bias: Rethinking the politics of the resource curse'. Project on Middle East Political Science, January. https://pomeps.org/borders-sovereignty-and-sample-selection-bias-rethinking-the-politics-of-the-resource-curse. [pdf]
Supplementary #ReasoningFallacyBiasSurvivorship [Wikipedia] "Survivorship bias occurs when a social or political process causes many units to drop out of the sample, while those which survive and enter the sample exhibit peculiar features, consistent with their survival, that makes them non-representative of the larger population. Left uncorrected, survivorship bias can lead to biased conclusions. Therefore, standard practice is faulty. Standard practice is to estimate a conditional probability function, such as the probability of autocratic survival conditional on access to resource rents, on all units in a cross-sectional and longitudinal data set that spans the globe. Using all of the relevant data would seem to assure most scholars that they are not inducing bias through selecting a biased set of cases; but with survivorship bias, the bias is built directly into the data set." Smith, B., & Waldner, D. (2019). 'Borders, sovereignty, and sample selection bias: Rethinking the politics of the resource curse'. Project on Middle East Political Science, January. https://pomeps.org/borders-sovereignty-and-sample-selection-bias-rethinking-the-politics-of-the-resource-curse. [pdf]
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