Why is this important?
Because it limits our ability to devise solutions to complex social problems by narrowing the scope by which we examine such problems, which, when insisted upon to inform and validate label-centric policies, chokeholds our ability to avoid self-referencing bias. In other words: when we examine complex real realities via statistical realities, not only do we limit our viewpoint to one lens of a kaleidoscope, but we also rely on this singular viewpoint to inform and validate the use of single lens label-centric statistical processes to uphold label-centric policies.
Thus, when the use of label-centric policies is defended via label-centric data while label-centric data concurrently validates the use of label-centric policies -- at what point do we exit this revolving tautological morass to explore the breadth of social problems through the multi-lens kaleidoscope of real reality?
The mere use of a tool to identify a problem does not automatically validate its use as such a tool -- no more than the mere use of a tool to validate itself does not automatically validate any problem it identifies.
Moreover, the process by which label-centric data is categorized, the process by which label-centric data is aggregated and manipulated, and every process in between is rife with limitations that circumscribe the veracity, accuracy, and utility of label-centric data to inform and remediate label-centric social problems.
The Whole Picture
At best, data and statistics are tools of mathematics and science that have been misguidedly appropriated by label-centric advocates to advance label-centric social agendas. At worst, without clarity, rigor, and ethical scrutiny at the helm of label-centric data collection and statistical aggregation, they are beguiling tools of label-centrist propaganda.
Slicing the Pie
In order to make sense of extraordinarily complex phenomena, comprehensible mathematic and scientific inquiry carves reality into digestible slices. In the case of label-centric data and statistics, this includes devising arbitrary systems of classification that confuse arbitrary delineations with intrinsic differences, i.e. race, socioeconomic status, age, gender, national origin, religion, marital status, educational attainment, sexual orientation, etc.
In other words, our bias of label-centrism informs our statistical systems of classification while affirming our preexisting label-centric preconceptions. Thus, when label-centric data and statistics confirm our preexisting label-centric biases, our single lens label-centric viewpoints become further entrenched by the false sense of credibility inferred by the use of an ostensibly scientific tool.
Tools for the Job
Unfortunately, while data and statistics are effective tools for mathematical and scientific differentiation, they are extraordinarily inept for cohesion. In other words, the value of statistical tools overwhelming scale with regard to statistical significances of differences. As for similarities? More often than not, conclusions of statistically insignificant differences are demonstrations of similarities by default rather than intention.
Not to mention that when arbitrary systems of label-centric classification lead to the collection and aggregation of label-centric data that propose or conclude that statistical differences exist between label-centric groups -- statistical processes afflicted by label-centric biases often fail to adequately address statistical differences that exist due to meaningfully real realities inconsistent with label-centric agendas. Instead, such differences are elevated as definitive proof of label-centric injustices requiring label-centric advocacy.
Numbers Are Sacrosanct
Through a process akin to money laundering, we've transformed a fallible mathematical and scientific process amenable to oversight, corrections, and improvements into an unassailably impartial stone tablet upon which incontrovertible facts are carved in service to label-centrist propaganda.
Thus: when label-centric crusaders launder bad data into good data -- whose reality is real -- and whose reality is fabricated?
More
Data is far from simply data. In the case of label-centric data, it's never simply data.
In the case of Jane Elliott's infamous 'brown eyes blue eyes' experiment, label-centric advocates would've collected label-centric data by eye color, because that was the definitive label that ostensibly separated the children in her class from each other. In so doing, such data would've demonstrated a measurable difference between these two groups vis a vis classroom performance and social adjustment.
However, we recognize Jane Elliott's classification of her students by eye color as an absurd and arbitrary delineation. Exactly as she intended. Yet, we perform such arbitrary classifications of ourselves and each other every day. Is that not what race, socioeconomic status, age, gender, national origin, religion, marital status, educational attainment, sexual orientation, etc. are? Arbitrary classifications that fuel label-centric equal but separate bigotry?
So: if Jane Elliott's classifications are absurd... why aren't classifications by race, socioeconomic status, age, gender, national origin, religion, marital status, educational attainment, sexual orientation, etc. absurd, too?
- M.
Addendum
The power of label-centrist propaganda to manipulate innocuous tools to advance self-serving agendas is so insidious, that we often fail to acknowledge that (1) it exists; (2) it influences our thoughts and decisions; and (3) it reinforces our label-centric intolerances.
In the end, what's more honest than a number? Isn't that what label-centric propagandists proselytize? That label-centric numbers are label-centric facts and label-centric facts are label-centric truths?
As long as we continue to believe that label-centric data is simply numbers cum facts cum truths -- we will fail to look behind the curtain where so-called truths are deftly massaged into digestible sound bytes for our mindless consumption. Indeed, when label-centric data is used to advance preferential considerations and differential advantages -- how likely is such data to be unadulterated?
Furthermore, how likely are any of us to know whether or not or how or if any data is adulterated? For adulterated data is indistinguishable from unadulterated data. In fact, despite the deluge of data driven sound bytes with which we're ceaselessly inundated, few modern uses of label-centric data are accompanied by total clarity (i.e. robustness of study design, validity of variables, integrity of data collection, etc.), including an appalling obfucation of the ridiculousness of 'representative' samples (i.e. n vs N and n(1) vs n(2)) upon which label-centric assumptions of label-centric injustices hinge.
Also concerning? The resounding absence of forthright disclosures regarding statistical predictability and reliability with regard to conclusions derived from label-centric data. Notwithstanding that label-centric data and statistical analysis rarely 'conclude' anything. Despite the fact that label-centric statistical analysis, in service to label-centric agendas, often merely (1) infer the existence of label-centric differences (which label-centric advocates decry as 'injustices') and (2) infer a possible relationship between our labels and label-centric 'injustices' -- label-centric data is resoundingly misused to conclude the existence of label-centric 'injustices' vis a vis our labels.
The bottom line? If label-centric biases drive our label-centric data -- how trustworthy and infallible is our label-centric data? And if label-centric data drives our label-centric remediation of label-centric injustices and if label-centric data is our proof that label-centric injustices exist and that label-centric advocacy is appropriate and just -- how incontrovertibly certain are we that injustices exist vis a vis labels and advocacy is just vis a vis labels and remediation is efficacious vis a vis labels?
In fact, what if label-centric data is label-centrist propaganda? If so, would we sanction its use to inform and validate social advocacy and public policy? Because we do. Moreover, what if label-centric data as an insidious tool of label-centrist propaganda surreptitiously corroborates our label-centric intolerances? If so, would we rely on it to inform our personal thoughts and decisions, as well as our public positions and policies of advocacy and governance? Because we do. Ultimately, what if label-centric data hinders human advocacy via overt and covert dissemination of bigotry? If so, isn't label-centric data problematic for the advocacy of equality for all? Because, in the end, isn't human advocacy unequivocally more conscionable than the advocacy of equal but separate?
- M.
Note
When candidates for the highest political office in America offer propagandized data to confirm a reality that we've already presumed a priori to be true (especially vis a vis our ideological alignment with numerous label-centric crusades) -- what does that say about our candidates, our label-centrism, and us?
Read more: Fact check: The first Democratic Debate (USAToday 10/14/15) (content originally posted here at FactCheck.org (10/14/15)).
One of the hot topics of today is the 'gender pay gap'. Yet, while unadulterated label-centric data does show statistical differences of pay across gender -- unadulterated label-centric data does not definitively conclude that this difference is wholly due to gender or gender bias. Period. Any such conclusion is a distortion of both statistical realities and real realities. Rather, there are a multitude of complex reasons why statistical differences exist within the unadulterated data for pay vis a vis gender.
Nevertheless, propagandist distortions like the derivative conclusion above are often guilefully crafted to propel tone-deaf label-centric crusades: Women are more likely than men to go to college, but still get paid less by Jillian Berman (MarketWatch 10/10/15). Advocacy for higher pay vis a vis gender within the singular context of women who've attained college degrees spotlight how self-righteously label-centric advocacy elevates self-serving label-centric agendas over far more grievous realities. (Likewise, advocacy for equitable pay vis a vis gender within the singular context of performers who earn millions highlight the overweening solipsism endemic to label-centric crusades.)
- M.
