John Cox, MA, Cascades
We done some fly-overs this last weekend, to go and see 100,000 Wild Horses decimate our Public Lands in America, or in the 11 Western States. We did not find 100,000 Wild Horses roaming the Range. We did not see the decimation, nor the Characteristics of lands being dominated, by neither Domestic Horses nor Wild Horses.
How did we get to the point of Wild Horse Overpopulation counts being so tremendously inaccurate, development of many false dynamic representations about Wild Horses, and so obviously based on piss-poor or downright erroneous base-data? We see the foundation material for data-sheets, erroneous statistical conversions leading to false conversations, and creation of false-narratives, as generic today; yet, still false, and very costly to taxpayers for things taxpayers receive no benefits from, compared to the wealth that Special Interests and corporate/government relationships provide.
Well, we must plunge-into the world of false information for specific purpose, and special interests, who mooch and thrive $$$ skimmed-off our Public Lands management paradigms – precisely. So, let us clarify statistics, or better yet, what we are seeing when we read DOI/BLM graphics and charts, excel sheets, and various amounts of other situations, that is nothing more than garbage-can-fodder.
We often jump to conclusions, as we assume the Base-Line Data gathered for the charts, graphics, or statistical excel sheets, are fundamentally sound, or correct. The simplistic question to ask is, “We disagree with the numbers you give us, as you state are reality-numbers. So, we need to see, or too confirm, the evidence from the data-gathered, used in the population counts.
As many of us see on our Public Lands, simply from observation while out there, DOI/BLM numbers are irresponsibly given to taxpayers, and fraudulently-erroneous. These false-numbers, or data, are then churned-up into a specific data-base. The false-numbers/data is then shoved into a Modeling Programs Application, rearranged to appear as reality. . . Keeping in mind, Modeling Program Applications are only as good as the data entered; which indeed, and in this case, obviously manipulated further, as well.
This neutralizes all Modeling-Data used in any type of Lands Management decision making process. The Wild Horses simply takes the brunt of this fraudulent activity, in both the corrupted DOI/BLM and corrupted non-profits, to make money, while blaming the Wild Horses for the ultimate destruction such false-information generates.
With DOI/BLM information given to the public, as if reality, the very foundation of their percentages of “increased-based-math”, as well as data used for Wild Horse Population Counts, is erroneous. It is, truthfully, flawed to the point of being incorrect, in total; or, to describe it simply, 1+1=132 is simply not correct . . .
The photo here is far more correct than any information we are obtaining from DOI/BLM or the associated corrupted non-profits, who actively support the over-population of Wild Horse Data, via their being paid $-millions to Shoot Pesticide into Wild Horses, and get-away with it. The Tragedy of False-Narratives moves on, and apparently up to this point, no one the wiser – except . . . So, we’ll break it down.
Let’s discuss Statistics. A or B when C or D may be as likely, when established upon a false-premise or incorrect data, then becomes no-option, or in statistics a “0” for false, with any numbers above “0” a truth, whether agreeable or not. We find the data-collected by DOI/BLM Wild Horse Counters, to be inaccurate, simply by counting the Wild Horses in each designated area, boots-on-the-ground counts, of 5 HMA’s before and after recent roundups. We find DOI/BLM counts very fraudulent, and the numbers increased, at times, X=10 the actual number on the range. We are also gathering more numbers in other HMA’s, and for future observation of the Fraudulent Activities by the DOI/BLM employees, in regard to Wild Horse Counts. All Wild Horse Advocates should be doing this, and all counts stored in a safe place – rather than, supporting the Shooting of Wild Horses with Pesticides, or giving Stallions GONACON Pesticide Sterilant!
For example, we are led to believe that a 20% birth rate increase over a one-year time period is a normal birth-rate for Wild Horses on our Public Lands. We are then being mis-led by false data. E.g. death rates overall of Wild horses on the range, roundup data, foal death at birth, yearling death rates, et al., are never assimilated as data within a foundational-statistical-set.
The fact is, the foundation-data corrupt, and positively-correlated with a higher income, more subsidies to ranchers, and more profits to corrupt non-profits. So this makes the very foundation of the Data-Base, erroneous, yet still input into the Modeling Programs as reality. Management decisions forced upon our Public Lands (paramount destruction) then made upon this false-data, run-through many processes, becomes corrupted in total. And this information is what the general public receives, and several folks in non-profits interpret to the general public as realities, and reasons to Shoot (for profits $$$) Wild Horses with Pesticides and other toxic chemicals via dart of pill form, into our Nations Wildlife and Wild Horses.
Did you know that those non-profits, I mention here, are doing the same things to our Wild Horses, that Wild Life Services is doing to much of our Wildlife in Wilderness areas? Let this “soak” in your mind for awhile.
We can then “conclude” that additional years of corrupted foundational-data-base information moves upward, never declines, moving to a much higher percentage-base, which “develops into” a greater wealth to the corrupt. However, there could also be a 3rd factor, such as a corrupt-willingness to mislead the taxpayer-base public in America, to believing an over-population of Wild Horses exists, which can also develop to experimentation, grants from public as well as private foundations for birth control use, or grants from government to accomplish both experimentation and experimental birth controls forced upon today’s guinea pigs, the Wild Horses – or deer, elk, coyotes, wolves, cougar, et al. . .
The more we accept the false-narratives, or the falsified foundation-data of Wild Horse over-population, we then observe more years of applied-misinformation as well as misinformation campaigns, driven by the corrupted non-profits, and achieved by the false foundation-data-base, which generates higher income for all the corrupted entities involved. In truth, or fact, it is the 3rd hidden variable that will lead us to incorrect conclusions about causality, or disruptions within out wilderness areas, or tremendous disruptions within Ecological Habitats.
Another inaccuracy with statistics is two variables may appear to be correlated, but really have nothing to do with each other. If you make enough comparisons between datasets, you are bound to find some interesting relationships that appear in-sync, yet, have no connection at all toward proper management or decisions based upon either circumstance. So once again, we find the correlation between statistical-data contrived, when false data-base material is used as if being factual information. A or B when C or D may be as likely, or appear as truth, when established upon a false-premise or incorrect data, then becomes no-option, or in statistics a “0” for false, with any numbers above “0” a truth, whether agreeable or not. Taking a “0” ad making it a one, within a date set, put into a surveyor’s term of math, “. . . if one starts an inch off, or away from the actual boundary marker, the measurement can develop into a 4 ft. or 5 ft. et al., error, depending on how far the survey goes, and the survey would never come around to its starting point, nor confirmed as correct, for factual closure. . .
For example, when we hear over many years about over-population of Wild Horse, the dynamics, we automatically associate Public Lands destruction by Wild Horses, or grass-lands devastated by them and due to over-population, as something factual (or to those unknowledgeable about Wild Horses). It then becomes much easier to believe; yet, conflicts with any type of credible foundation-base-dynamics of truthful data-gathering, or stated simply, conflicts with the reality about what a horse is and is not. However, there could also be a 3rd factor here also, such as destruction by an overpopulation of Cattle or Sheep on Public Lands, and the horses blamed for it . . . Indeed, this 3rd hidden-variable can lead us to incorrect conclusions about causality, yet appears as stable information within the statistical format.
“We’ve all heard the advice that correlation does not imply causation, but even when there is a causal effect, it’s often uncertain which way it goes. Many questions arise here . . . Questions of cause are answered by randomized controlled trials, not by observational studies where we cannot rule out additional factors that we do not measure. To avoid being misled, approach correlations between variables with skepticism by looking for confounding factors. Humans like neat, causal narratives, but that is usually not what the data is telling us. Ultimately, we see both DOI/BLM employees, as well as corrupt non-profit people, avoid truth within many ways, and then offer false-narratives to coverup their exploitative and corrupted behaviors. If you do not tell a person something that you should or just telling part of it when asked about it, so that they know a little bit, but not the whole truth and you call it hiding it. Is it just hiding or lying? In truth, it is deception, and deliberately misleading, and the result would be just the same as a lie.
[Permission to share article in its entire context, Permission upon request-only to share partial context, and only when reference to author and entire article given.] Article Published – 2023