Human Action and Cognition as Synthetic A Priori Knowledge Necessary for Understanding Data Ontology
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The discipline of data ontology deals with the facts of data; what data is. What is often neglected within this field is the praxeological and epistemological aspects of data ontology; that there is an essence of human action and cognition that is real phenomena, that exists, is governed by natural law, that all analysis of history must be qualitative as opposed to quantitative, that statistics must remain in disciplines that have certainty and regularity, such as the natural sciences. Data ontology, in order to produce correct data, must incorporate these philosophic concepts into its body of knowledge or else descend into a lowly field of circular justification for Pythagorean prophesy. Consider the fallacious standardized tests, in particular IQ; they are scientistic mechanisms to “measure” things without form, like human intelligence. First, before the analytic history of the IQ test, the synthetic theory must be defined. Human intelligence is phenomenal; it is not formal, a quality rather than quantity. As Caenan Perez discusses in On Natural Order; vertical diversity is an ontological fact of human cognition, action, and fundamental nature. Man can personally synthesize, without any sensory observation, that he is more or less different from each man, that each individual person has ends and employs means to achieve those ends, that he values certain ends more than others, that he employs different means to achieve those ends. This ontological structure is phenomenal a priori, and because of this fact, takes form infinitely. Numbers from data could not possibly bring any form to this phenomenon that would be real, that would provide any science or knowledge. It is what the great constructivist Paul Lorenzen referred to endearingly as “epistemologically worthless symbolic games”. This did not deter men like Francis Galton with his behavioral genetics, or Adolphe Quetelet, who sought to import the methods of astronomy and physics into human philosophy with his ugly creation of l’homme moyen, or man of averageness; this Averageman would be measured around a Gaussian distribution, or bell curve, with standard deviations determining the quantities of average composition he has. One must hasten to add that these men were anything but average in their qualities and character, perhaps committing to a most arrogant solipsism that they would somehow be the progenitors of these laws, regulatory bureaucrats, if you will. This is significant, for intuitionist logic and cognitive reflection would be able to reason phenomenal concepts and qualities, instead of hammering down man into a false mechanical mold to suffice mathematical games. Late 19th century psychologists of Germany and France, such as Hugo Munsterberg, Wilhelm Wundt, Alfred Binet, and Theodore Simon, would still use Gaussian-Queteletean statistics to inform their psychometrics. These psychometric products would unfortunately be picked up by American psychologists and eugenicists such as Ellwood Patterson Cubberly, Edward Thorndike, Lewis Terman, and the great bulk of education and teacher’s colleges at research universities across the United States. Here they would use statistical methods in historiometric fashion to manipulate the political economic order into their favor by presenting prefabricated (which means human action and cognition were involved) “empirical evidence” and “historical backing” for their nonsensical views on such things as race and class. Data ontologists can learn likely the most important lesson they can about their data and their models by acquiring erudition in the history of education, due to its heinous track record of poor mathematics and ontological ignorance. Data ontology must affirm as its most basic axiom the phenomenal reality of human action and cognition and the role of data as being confined only to certain events with regularity. To ignore this reality would be performative contradiction, as denial is apriori free will of human action and cognition. The data ontologists job is to inform those dealing with numbers that they cannot overstep their boundaries into concepts and phenomenon that are inappropriate for the measure of numbers, and must acknowledge the human actor and cognitor element behind even correct data research and science and to scrutinize initial data with this in mind.
