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# The New Normal Is Quite Possibly A Code Word For Depopulation

### Does 'The New Normal' = 'Depopulation'? The ultimate guide

### Full Video Report:

I’m guessing you heard the phrase “the new normal” dozens if not hundreds or thousands of times during the lockdown. We were inundated with the phrase in news reports, politicians speeches, talk shows, TV, etc. Check out this 13 minute compilation video to get a sense of how strangely this contrived phrase was shoehorned into popular culture.

I remember I saw a comment under a video somewhere. It said something to the effect that “**depopulation** and **the new normal** match in the 4 major ciphers.” Spending enough time on the internet- I knew what they were claiming and the importance of this if it were true. So I went to a popular site called Gematrinator.com to calculate the 4 major ciphers for each of these phrases. ‘Depopulation’ and ‘the new normal’, both 12 letters, indeed matched in what are commonly called ‘the 4 major ciphers’:

If you don’t know the ciphers, let’s bring you up to speed. With all these gematria ciphers, letters are given different values and then a unique number can be calculated for any word or phrase. Each of these major 4 ciphers uses the alphabetic order of the letters in the English language in various ways. The two reverse ciphers put the alphabet backwards starting at z.

## Cipher 1: English Ordinal:

This is the simplest cipher. For every letter we add which letter # in the alphabet it is to our total. A = 1, B = 2, C = 3 … Y = 25, Z = 26. Remember when you see “Ordinal” in this article it just means “alphabetic order”

## Cipher 2: Full Reduction:

For every letter we encounter in the full reduction cipher, we start with the letter # from the alphabetic order. If the letter’s number is more than one digit, we “reduce” it down to a single digit. For example R is the 18th letter so in the Full Reduction cipher it equals 9. S is the 19th letter, and we actually have to reduce it twice, adding the digits together each time. 1+9→10→1+0→1 So S has a value of 1. Here are all of the letters’ values:

Interestingly it just goes from 1-9 over and over.

## Cipher 3: Reverse Ordinal:

This is another very simple cipher. For every letter we add which letter # it is in the reversed alphabet to our total. A = 26, B = 25, C = 24 … Y = 2, Z = 1

## Cipher 4: Reverse Full Reduction:

The Reverse Full Reduction cipher reduces down to a single digit each letter’s # in the reversed alphabet. So for example H is the 19th letter of the reversed alphabet, and that reduces to 1: 1+9→10→1+0→1

## What Are The Odds?

So now to the question: what are the odds that two phrases (especially Depopulation & The New Normal) match in these ciphers? Let’s examine what the odds are for each of these 3 situations:

**Two phrases match in all 4 ciphers**Two phrases match in English Ordinal cipher

Two phrases match in English Ordinal Cipher & Full Reduction Cipher

### Methods

I wrote a script to simulate different phrase possibilities using a bank of common English words and then for each phrase it calculates the values for each of the 4 ciphers. Then it compares the different values and calculates the percentage of the times they matched. Here’s my script:

Note there is one key line that I change to test for the matching of different ciphers :

The above checks if ALL FOUR of the ciphers are matching. I change this for other simulation runs. For example, if I only care if the English Ordinal cipher matches, this would be the proper logic:

## Length A Key Factor

As you’d expect the length of the two words is a huge factor as to how many of the ciphers match. In fact, it’s IMPOSSIBLE for the words to match in all 4 major ciphers if they are not exactly the same length.

If two words have the same length and match using the English Ordinal cipher, then they will ALWAYS match in the Reverse Ordinal cipher. If they are different lengths it is IMPOSSIBLE for them to also match in both the Reverse Ordinal and English Ordinal ciphers. Here’s how that works with two examples:

The top example demonstrates that if two 4 letter words match using English Ordinal will always also match in Reverse Ordinal. The second example shows how two different lengthed words that match in English Ordinal will never match in Reverse Ordinal.

## Matching Phrases

A feature of the script is it logs all the matches. Here are a few interesting example matches:

peaceful monster & newspaper flames MATCH ALL CIPHERS w/ 173,65,232,79

bolt accent lip & boredom castle MATCH ALL CIPHERS w/ 132,51,219,75

saint & tears MATCH ALL CIPHERS w/ 63,18,72,36

civilization & receiver tips MATCH ALL CIPHERS w/ 149,68,175,76

occupation & war eyelash MATCH ALL CIPHERS w/ 117,45,153,54

mission sea zoom & turtle link open MATCH ALL CIPHERS w/ 192,66,186,78

So it’s certainly possible for two words/ phrases to match in all 4 ciphers. Let’s run the simulation a few times and look at our results.

# Simulation Results

Each run generates upward of 3.7 billion unique pairs of phrases and sees how often matches are seen and breaks the results down for words of similar (or less similar) lengths. I did 2 runs for each to see how much variation there is between runs.

### Distribution of Lengths for Generated Phrases

I wanted to target phrases around 12 letters long like the pair we are investigating: ‘depopulation’/ ‘the new normal’. This results in lacking data for very small words to prioritize for longer phrases. Later on I run another simulation using randomized letters to fill in this gap a bit.

## Matching In All Ciphers

#### Run #1:

BREAKDOWN BY LENGTHS

Same length: 662894 of 535033338 => 0.124%

Length 1 diff: 0 of 929636122 => 0.0%

Length 2 diff: 0 of 753987830 => 0.0%

Length 3 diff: 0 of 562526534 => 0.0%

When both have length of 3: 10 of 1722 => 0.581%

When both have length of 4: 382 of 61256 => 0.624%

When both have length of 5: 1362 of 284622 => 0.479%

When both have length of 6: 2786 of 818120 => 0.341%

When both have length of 7: 4474 of 1395942 => 0.321%

When both have length of 8: 6628 of 2810652 => 0.236%

When both have length of 9: 15558 of 7887672 => 0.197%

When both have length of 10: 39518 of 23112056 => 0.171%

When both have length of 11: 74988 of 48853110 => 0.153%

When both have length of 12: 118540 of 88953192 => 0.133%

When both have length of 13: 140114 of 114586320 => 0.122%

When both have length of 14: 136216 of 124133022 => 0.11%

**TOTALS** 662894 of 3785387150 => 0.0175%

#### Run #2

BREAKDOWN BY LENGTHS

Same length: 647406 of 533939716 => 0.121%

Length 1 diff: 0 of 924088888 => 0.0%

Length 2 diff: 0 of 747508858 => 0.0%

Length 3 diff: 0 of 556635742 => 0.0%

When both have length of 3: 6 of 1560 => 0.385%

When both have length of 4: 378 of 61752 => 0.612%

When both have length of 5: 1048 of 269880 => 0.388%

When both have length of 6: 2928 of 823556 => 0.356%

When both have length of 7: 4378 of 1417290 => 0.309%

When both have length of 8: 6972 of 2871330 => 0.243%

When both have length of 9: 15046 of 7859612 => 0.191%

When both have length of 10: 37308 of 21850950 => 0.171%

When both have length of 11: 73252 of 48699462 => 0.15%

When both have length of 12: 113500 of 84686006 => 0.134%

When both have length of 13: 136146 of 114137172 => 0.119%

When both have length of 14: 134540 of 125406402 => 0.107%

**TOTALS** 647406 of 3765970056 => 0.0172%

#### Key Stats

When the phrases had the same length they matched 0.124% of the time. If the phrases are of different lengths, the likelihood of matching is actually 0%. This is because different lengthed phrases cannot match in both English Ordinal and Reverse Ordinal, as we showed earlier in this article.

For any two random phrases tested, the percentage likelihood of them matching in all 4 major ciphers was 0.0175% which is a rate of 1 every 5700. Keep in mind our targeted phrase lengths were mainly 10-14 letters as discussed earlier. Again, if two phrases have different numbers of letters, they can’t possibly match in all 4 ciphers.

#### Depopulation & The New Normal

Since the two phrases are the same length (12 letters) let’s especially hone in on the likelihood of two 12 letter phrases matching in ALL FOUR ciphers. According to this simulation it’s just 0.133% which is a rate of approximately 1 per 750

Keep in mind it’s not a given that the word in question for reducing the population and the phrase “the new normal” have the same number of letters. What are the odds of that? I don’t know, but say hypothetically it’s a 10% chance the words for these two concepts are equal lengths. That would take the odds of two equal length strings matching in all the ciphers, which is 0.124% per run #1 of the simulation, and we’d multiple that by 0.1 to account that that the equal length scenario only happens 10% of the time. So the odds of both happening together in this hypothetical example is just 0.0124% or approximately 1 every 8,065 times.

Either an amazing coincidence or something much more sinister.

## Matching In English Ordinal

#### Run #1:

BREAKDOWN BY LENGTHS

Same length: 6406746 of 530664318 => 1.21%

Length 1 diff: 10542822 of 922072556 => 1.14%

Length 2 diff: 7071942 of 745843790 => 0.948%

Length 3 diff: 3822060 of 556603256 => 0.687%

When both have length of 3: 42 of 2070 => 2.03%

When both have length of 4: 1200 of 58322 => 2.06%

When both have length of 5: 5428 of 291060 => 1.86%

When both have length of 6: 16246 of 934122 => 1.74%

When both have length of 7: 23324 of 1419672 => 1.64%

When both have length of 8: 40966 of 2803950 => 1.46%

When both have length of 9: 113892 of 7927040 => 1.44%

When both have length of 10: 297854 of 21935172 => 1.36%

When both have length of 11: 652914 of 50190140 => 1.3%

When both have length of 12: 1057456 of 85312932 => 1.24%

When both have length of 13: 1365676 of 113220240 => 1.21%

When both have length of 14: 1460266 of 125294442 => 1.17%

**TOTALS** 30163118 of 3761552892 => 0.802%

#### Run #2:

BREAKDOWN BY LENGTHS

Same length: 6474746 of 535999322 => 1.21%

Length 1 diff: 10603550 of 926148298 => 1.14%

Length 2 diff: 7176946 of 752245408 => 0.954%

Length 3 diff: 3873692 of 561690416 => 0.69%

When both have length of 3: 42 of 1980 => 2.12%

When both have length of 4: 1110 of 53130 => 2.09%

When both have length of 5: 5908 of 318660 => 1.85%

When both have length of 6: 14560 of 852852 => 1.71%

When both have length of 7: 22948 of 1377102 => 1.67%

When both have length of 8: 40430 of 2710962 => 1.49%

When both have length of 9: 117948 of 8000412 => 1.47%

When both have length of 10: 295420 of 21944540 => 1.35%

When both have length of 11: 656134 of 50488130 => 1.3%

When both have length of 12: 1070900 of 86016350 => 1.24%

When both have length of 13: 1361024 of 113731560 => 1.2%

When both have length of 14: 1429384 of 122666700 => 1.17%

**TOTALS** 30471404 of 3786002430 => 0.805%

#### Key Stats

When just looking at the most basic ciper, English Ordinal, it’s a 1.21% chance of two equally lengthed phrases matching. The odds go down the larger the difference between the phrases’ lengths:

There is a 1.14% chance of matching in English Ordinal for phrases with a length difference of 1

There is a 0.948% chance of matching in English Ordinal for phrases with a length difference of 2

There is a 0.687% chance of matching in English Ordinal for phrases with a length difference of 3

#### Depopulation & The New Normal

Since the two phrases are the same length (12 letters) the likelihood of them matching in the English Ordinal cipher, according to this simulation is 1.24%

## Matching In English Ordinal & Full Reduction

#### Run #1:

BREAKDOWN BY LENGTHS

Same length: 1869194 of 529557316 => 0.353%

Length 1 diff: 2923982 of 919585760 => 0.318%

Length 2 diff: 1659852 of 747590056 => 0.222%

Length 3 diff: 668194 of 561524468 => 0.119%

When both have length of 3: 22 of 1260 => 1.75%

When both have length of 4: 450 of 47306 => 0.951%

When both have length of 5: 2468 of 287832 => 0.857%

When both have length of 6: 6274 of 860256 => 0.729%

When both have length of 7: 9364 of 1405410 => 0.666%

When both have length of 8: 15802 of 2854410 => 0.554%

When both have length of 9: 38342 of 7854006 => 0.488%

When both have length of 10: 107452 of 23975712 => 0.448%

When both have length of 11: 207078 of 50972460 => 0.406%

When both have length of 12: 319490 of 86257656 => 0.37%

When both have length of 13: 387934 of 111123222 => 0.349%

When both have length of 14: 399756 of 121980980 => 0.328%

**TOTALS** 7343566 of 3764129256 => 0.195%

#### Run #2:

BREAKDOWN BY LENGTHS

Same length: 1854580 of 530604420 => 0.35%

Length 1 diff: 2899774 of 916691804 => 0.316%

Length 2 diff: 1655074 of 740700308 => 0.223%

Length 3 diff: 666504 of 551216304 => 0.121%

When both have length of 3: 20 of 812 => 2.46%

When both have length of 4: 574 of 54990 => 1.04%

When both have length of 5: 2664 of 318660 => 0.836%

When both have length of 6: 5900 of 805506 => 0.732%

When both have length of 7: 9122 of 1379450 => 0.661%

When both have length of 8: 15408 of 2750622 => 0.56%

When both have length of 9: 33988 of 6935322 => 0.49%

When both have length of 10: 98354 of 22217082 => 0.443%

When both have length of 11: 189284 of 47616900 => 0.398%

When both have length of 12: 321480 of 85054506 => 0.378%

When both have length of 13: 388108 of 112497842 => 0.345%

When both have length of 14: 406436 of 124601406 => 0.326%

**TOTALS** 7303290 of 3726430980 => 0.196%

#### Depopulation & The New Normal

Since the two phrases are the same length (12 letters) let’s note the percentage of 12 letter word pairs that match in the English Ordinal & Full Reduction ciphers. According to this simulation 2 random 12 letter phrases matched in the English Ordinal & Full Reduction ciphers 0.37% of the time

# Another Approach

To try to understand this better and double check my work I wanted to come up with the chances of each cipher matching with random 12 letter words. In this case I just used 12 random selected letters. For example one of the words was DVBRXXSRAAPU.

Here we examine how likely these cipher matches are:

Random 12 Letters Match Likelihood w/ Cipher:

English Ordinal: 1.08% Match

Full Reduction: 3.23% Match

Reverse Ordinal: 1.08% Match

Reverse Full Reduction: 3.23% Match

My next question is: if the 12 letters match in a cipher, what is the percentage it will match in another cipher too. I altered the simulation a bit to test:

When 2 Words (12 Random Letters) MATCH In English Ordinal:

Full Reduction: 30% Match Likelihood

Reverse Ordinal: 100% Match Likelihood

Reverse Full Reduction: 29.92% Match Likelihood

When 2 Words (12 Random Letters) MATCH In Full Reduction:

English Ordinal: 10.23% Match Likelihood

Reverse Ordinal: 10.23% Match Likelihood

Reverse Full Reduction: 37% Match Likelihood

When 2 Words (12 Random Letters) MATCH In Reverse Ordinal:

English Ordinal: 100% Match Likelihood

Full Reduction: 30% Match Likelihood

Reverse Full Reduction: 30% Match Likelihood

When 2 Words (12 Random Letters) MATCH In Reverse Full Reduction:

English Ordinal: 9.96% Match Likelihood

Full Reduction: 36.8% Match Likelihood

Reverse Ordinal: 9.96% Match Likelihood

Here’s an important simulation we can run: For pairs of random 12 letter words, what is the percentage likelihood they match in all of the ciphers?:

0.11912% of pairs of random 12 letter words match

What about other lengths of words? Here are the percentage likelihoods of pairs of words comprised of random letters matching on all 4 ciphers when they have the same lengths, broken down by word lengths:

3 Letters: 7021 / 769440 = 0.913% Match Likelihood

4 Letters: 4689 / 769486 = 0.61% Match Likelihood

5 Letters: 3438 / 769424 = 0.447% Match Likelihood

6 Letters: 2579 / 768862 = 0.335% Match Likelihood

7 Letters: 2131 / 769466 = 0.277% Match Likelihood

8 Letters: 1719 / 768026 = 0.224% Match Likelihood

9 Letters: 1442 / 768306 = 0.188% Match Likelihood

10 Letters: 1215 / 770859 = 0.158% Match Likelihood

11 Letters: 1024 / 768935 = 0.133% Match Likelihood

12 Letters: 895 / 769058 = 0.116% Match Likelihood

13 Letters: 819 / 769209 = 0.106% Match Likelihood

14 Letters: 743 / 769263 = 0.0966% Match Likelihood

15 Letters: 643 / 769666 = 0.0835% Match Likelihood

## Conclusion/ Key Findings

It’s very strange how the phrase “The New Normal” was used so relentlessly and in such a contrived, unnatural fashion. It’s extremely notable that the phrase matches in all 4 of the major ciphers with “depopulation.” Especially important if we in fact end up seeing the depopulation play out right after the contrived use of “the new normal”

The major players behind the plandemic response are trying to cap the world population (and possibly worse than that). In 2009, some big-name super wealthy & powerful billionaires met in secret in New York to fight what they call the problem of overpopulation

This article set out to quantify the likelihood of the phrases matching in all these ciphers. While it’s possible this is just a coincidence, after much contemplation and research, I find it very unlikely.

There are two possibilities:

“The new normal” is intentionally being used as a code word for depopulation

Just a very, very unlikely coincidence

How unlikely would the coincidence be? It’s hard to quantify this. There are, of course, a lot of words/ phrases of interest. The chance of any two of these phrases matching is near zero. But of course some of them will watch up the more you check.

To me, the 2 phrases in question are extremely important and seemingly very intertwined. Depopulation is the type of agenda that WOULD need a code phrase to talk about over people’s heads. The New Normal being a code word for depopulation would make sense, in my opinion.

In my simulation outlined earlier, using a bank of English words to generate random phrases here are the key findings:

0.124% chance for two phrases of the same length to match in all 4 ciphers

0% chance for two phrases not of the same length to match in all 4 ciphers

Since our two phrases are the same length, I ran a simulation using random letters to calculate odds of matching in all 4 ciphers:

0.22% chance for two phrases of the same length to match in all 4 ciphers

Let’s also explore the percent chance of this by length of the word. According to the simulation, here are the percentage likelihoods of the two identically lengthed words matching, broken down by word length:

Length of 5 letters: 0.479% Match In All 4 Ciphers

Length of 6 letters: 0.341% Match In All 4 Ciphers

Length of 7 letters: 0.321% Match In All 4 Ciphers

Length of 8 letters: 0.236% Match In All 4 Ciphers

Length of 9 letters: 0.197% Match In All 4 Ciphers

Length of 10 letters: 0.171% Match In All 4 Ciphers

Length of 11 letters: 0.153% Match In All 4 Ciphers

Length of 12 letters: 0.133% Match In All 4 Ciphers

Length of 13 letters: 0.122% Match In All 4 Ciphers

Length of 14 letters: 0.11% Match In All 4 Ciphers

### The Bottom Line

For 12 letter phrases, the odds of them matching in all 4 ciphers is 0.133% (simulation run #1)

But what are the odds both phrases have 12 letters? Since we are pursuing that line of thought, it’s probably better to leverage the result that equal lengthed phrases matched in all 4 ciphers 0.124% of the time. One other question of importance is: what are the odds two phrases match in length? I don’t have that number for us today, but the percentages would be multiplied together to calculate the odds our phrases match.

For example if there’s a 15% chance of our two phrases being the same length: the overall likelihood could be calculated: 0.124% x 0.15 = 0.0186%. This would be an approximate rate of 1 match for every 5376

I guess we’ll have to watch the data to see. Personally, I’m extremely worried.

### Bonus Thoughts:

A horror movie I hadn’t seen in years peaked out of my subconscious the other day. It’s called Population 436 by the huge company Sony Pictures Home Entertainment. It seems to me to be overtly referencing the depopulation agenda. The Rockefellers might be somewhat referenced by the name of the small town where the movie takes place, Rockwell Falls. Basically the town kills a townsperson whenever there is a new baby or somebody moves in. All to keep the population at 436. I’ll write more about the depopulation agenda, but one example quote from the Kissinger Report:

Kissinger, closely aligned with the Rockefellers, dubbed this depopulation plan the “The World Population Plan of Action” 8 billion is the proposed population ceiling.

But I only bring this up to share one scene from the movie, where reduction gematria is being utilized by the census worker in his research into the impossibly consistent population of the town:

So he reduces the population: 436, two times, down to a single digit.

4+3+6=13

1+3=**4**

This is what the Full Reduction & Reverse Reduction ciphers do as well. Again, Depopulation and The New Normal match in the major FOUR ciphers.

And right before that scene the film makers made sure to show this bookshelf at the city records office with “History of Numerology” that the main character grabs from the shelf

## The New Normal Is Quite Possibly A Code Word For Depopulation

AI might have come up with it I suppose.

The Corona End Game

The Truth Behind The Symbols

https://lionessofjudah.substack.com/p/the-corona-end-game

1992

https://lionessofjudah.substack.com/p/the-corona-end-game-addendum