<p>Some research to examine downvoting was performed. The top 500 CV users with respect to reputation were examined with respect to clusters of downvotes using visual inspection then k-means for two populations performed with Mathematica v. 13.0.0.
<a href="https://i.stack.imgur.com/sjW4H.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sjW4H.png" alt="enter image description here"></a></p>

<p>This shows three clusters. Note that reciprocal scaling was used on the <span class="math-container">$y$</span>-axis to produce a dependent variable distribution that was indistinguishable from a uniform distribution. Also, square root transformation of the <span class="math-container">$x$</span>-axis data removed much of the heavy right-tailed appearance. Visual inspection was used to identify the zero percentage downvoter population as cluster analysis proved problematic for that purpose. Subsequently, k-means did a good job of isolating two populations that were relatively independent of reputation level. </p>

<p><strong>Addendum</strong> Given the copious feedback, I did further processing. @NickCox suggested using folded square root of percentage downvotes, <span class="math-container">$\sqrt{x}-\sqrt{100-x}$</span>, on the <span class="math-container">$x$</span>-axis. This worked well in SPSS in version 13, (old but good), using two-step cluster analysis followed by k-means for a variable number of clusters with the best clustering chosen by BIC. This show the following plot. Two-step cluster analysis showed that classification by reciprocal reputation failed <span class="math-container">$t$</span>-testing. The cluster data forwarded to k-means then yielded two clusters as below. </p>

<p><a href="https://i.stack.imgur.com/fq4ha.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/fq4ha.png" alt="enter image description here"></a></p>

<p>This has similar cluster results to those achieved prior. Once again the visually identified clustering at 0% downvoting is obvious, but in this case was selected <em>post hoc</em>. The percentage breakdown is <a href="https://i.stack.imgur.com/IT1kb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/IT1kb.png" alt="enter image description here"></a></p>

<p>@NickCox suggests that downvoting is a method of removing low quality posts. Therein is a big hint as to what is going on. What's in a downvote? On a social media site based not on evidence but personal taste, voting is like/dislike. On an evidence based site, more like ours, a vote should, to my way of thinking at least, more plausibly suggest correct/incorrect material. What appears to me to be lacking, and needed on our site is some method of indicating material that is contributory/non-contributory to the site. For example, suppose a question is <strong>Why does <span class="math-container">$2+2=4$</span>?</strong>. Obviously, this is not on the correct/incorrect axis but it appears to add little to the site's compendium of knowledge; it is non-contributory. Thus, currently downvoting is a mixture of two axes "like/dislike" and "correct/incorrect". That, I think, is what allowed k-means to identify the split in population at circa 12% downvoting rate. Is there any evidence to support this? The SPSS histogram resulting from the three clusters, one of them identified visually at 0% is... </p>

<p><a href="https://i.stack.imgur.com/tJjz6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tJjz6.png" alt="enter image description here"></a></p>

<p>Next, we ask if the non-0% clusters follow some recognizable distribution type. Mathematica's FindDistribution routine was applied to this data and it selected two Weibull (squared) distributions as the most likely fits from its extensive library of distributions. That is shown next. 
<a href="https://i.stack.imgur.com/8mgGn.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/8mgGn.png" alt="enter image description here"></a></p>

<p>Weibull distributions also best fit the original downvote data without square rooting. No matter how I tried to fit the histogram data, the results were not significantly different from Weibull distributions. In absolute terms, this cannot be a Weibull distribution as such a distribution has values beyond 100% downvoting. However, the Weibull result may be relevant as one interpretation of the Weibull distribution relates to <a href="https://en.wikipedia.org/wiki/Diffusion_of_innovations" rel="nofollow noreferrer">diffusion of innovation</a>. As per <a href="https://en.wikipedia.org/wiki/Weibull_distribution" rel="nofollow noreferrer">Weibull</a> "In the context of diffusion of innovations, the Weibull distribution is a "pure" imitation/rejection model...In the context of the diffusion of innovations, this [<em>Sic</em>, <span class="math-container">$\alpha&gt;1$</span> (As it is herein), where <span class="math-container">$\alpha$</span> is the Weibull shape parameter] means positive word of mouth: the hazard function is a monotonically increasing function of the proportion of adopters. The function is first convex, then concave with an inflexion point at <span class="math-container">$1-e^{-1/\alpha}$</span> [<em>Sic</em>, notation translated]"  </p>

<p>Thus, there is statistical evidence for two groups of adopters beyond the 0% group, who are "non-adopters". This is evidence for an admixture of downvoting strategies that does not agree with the opinion as voiced in some comments that such a phenomenon does not exist in this data. Such an admixture of voting patterns for downvoting is opaque to the recipient of such votes. That is, although we can sort this out statistically, the individual downvote recipient has no such clues, which implies that downvoting is not efficient and not clean from the user's POV. One remedy for this could be to shunt some of this downvoting in the high downvoting rate category to a closure vote on the contributory/non-contributory opinion axis as this would provide clarity as to what a downvote means for the user while doing a more direct job of addressing material that is non-contributory for the more expert reviewer, as follows.</p>

<p><a href="https://i.stack.imgur.com/CpAZH.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CpAZH.png" alt="enter image description here"></a>     </p>

<p>The selections <code>off topic</code>, <code>too broad</code> and <code>primarily opinion based</code> are  currently being used as alternatives for <code>not-contributory</code>. Those alternatives, even taken together, do not span <code>not-contributory</code> such that current practice is not really addressing the need to prune the site for content that is just not up to par value for expert opinion; <code>low quality</code>, <code>not expert question</code> and so forth.</p>

<p>For answers, there is no closure voting <em>per se</em>. However, there is flagging which provides similar functionality, but requires review at my reputation level.   </p>

<p>I will continue editing and collating to de-bulk and organize a bit better. </p>
