As with all statistical results (inductive conclusions),
network “maps” are commonly interpreted as facts (absolutely correct
statements, true propositions).
Thus, conclusions drawn from “mapped” data are usually
mistaken for scientific facts.
This tendency is probably based on the common presumption
that quantitative (numerical) statements are more reliable.
In fact, quantification is no more than replacing impresion
and narrative by measurement, and measurement per se is neither precise nor
reliable by definition.
An important mediator and a potential source of bias: Relevance
to practice, i.e. decision-making—with varying degrees of social economic
cultural impact. Possible alternatives for the purpose of data collection—and mapping
big data:
-
Applicable to policy-making, i.e.
“translational”—the procedure of “translation” presents additional
logical/mathematical challenge, since translation, by definition, involves
induction, generalization.
-
Remotely, if at all, associated with
policy-making
-
Built specifically to inform policy-making
I agree,
YanıtlaSilData mapping is a comprehensive tool that enables us to analyse i.e. the Market.(determining the optimal price for a commodity in big data of goods and so forth) A tool also does not need to be precisely true in order to be useful. I think that the reliability of data mapping is supposed to be in a place that we consider as satisfaying for a certain purpose in a scale of wrong-...-true, with a room for disagreement.
On the web, where the supply of information is huge, extremely diverse in origin, and ever-changing, the ways without mapped data is not very efficient.
Thank you