🧱
This article is a bit on the stern side, so I’ll preface it with something that might soften it, my current theory, then the stern bit.
In about April 2023 we see the Boosted cohort really start to pick up in per-capita rates across everything – cases, hospitalisations, and deaths, even in the younger cohorts. At the time I was regularly publishing the weekly data, and a couple of other people were getting their own analysis out on Efficacy. We didn’t really have any Safety data aside from the OECD mortality tool and the HSU spreadsheet, which was showing around 0.74 excess deaths per 1,000 doses, and that Excess Mortality was showing up after vaccination, and continuing after Covid mortality, in a relationship with each Booster rollout We were working off the public data streams, but showing them in a form that HNZ did not – per-capita. The argument of “there’s way more Vaccinated people” falls down when you show it by Rate.
An OIA for all-cause mortality by dose count and days since last dose to death was likely in the system (it came out soon after the below happened), and there was already lots of talk about Vaccine Harm. So, what to do, from the perspective of the authorities desperate to preserve the Safe and Effective narrative?
Set up a situation where an impressionable data person who only has access to a partial dataset that is focused on the end-of-life facilities more than the general population, sees “lots of deaths”. Being a decent human, he will do whatever it takes to get that noticed, up to the point of exporting it in violation of his employment contract.
He will then spend time sending it to various people, each of them will tell him that the data he has isn’t actually showing full proof, and to put it away and hope it’s not noticed. He will keep trying people, because this NEEDS to be shown to somebody. Eventually, he will hit upon one of the already-discredited conspiracy loons, who will amplify his incorrect signal.
The authorities, knowing the fallacies in the dataset, will be able to easily point out those issues.
This will lead to discrediting all data analysis on Vaccine harm, and hopefully those who have been showing the lack of efficacy jump on board, so their reputations are also tarnished.
But then again, that would never happen, right?
I’ve been accused of some things lately.
Attacking “the only person brave enough to release The Data.”
Taking orders from Bloomfield.
Being a pharma shill.
A bad actor.
Let’s set a few things straight.
🧵 I was already doing the work. Long before Barry hit Export.
In 2022, I was publishing weekly cohort-aligned breakdowns of:
COVID cases,
Hospitalisations,
ICU admissions,
all by dose count, using the Ministry of Health’s own Case Demographics and Vaccination datasets.
When mortality data by dose count was made available in March 2023, I moved into per-capita death analysis — by vaccination status, by age group, by time since dose.
I wasn’t just playing with spreadsheets. I was:
Reconstructing HNZ’s dose progression rules from rollout snapshots.
Aligning cohort entry to dose timing.
Calling out anomalies in data structure and metadata without relying on any narrative.
By the time the “stolen data” was taken in mid-2023, I had already:
Built up a reasonable following of 4,500+ people on Twitter,
Shared 100+ hours of work on clean, structured data breakdowns,
Been one of three people in NZ (that I am aware of) publicly publishing this level of cohort-aligned COVID outcome work.
Barry didn’t come to me. I’d have taken a 20 minute look (like I did when it leaked) and told him it really wasn’t complete enough to draw conclusions from, and to delete it and try an OIA for the data.
🎭 Instead, he went to the people who told him what he wanted to hear.
First came the October 2023 MOAR video: Winston Smith, the anonymous saviour, with “undeniable proof of tens of thousands of vaccine deaths.”
Then came the post-election November release, fronted by Liz Gunn and Steve Kirsch, claiming they had “the smoking gun.”
Except… they didn’t.
I saw the dataset briefly, before the injunction.
It was a Pay Per Dose snapshot. ~4.1 million rows. Less than 25% of total doses.
Missing:
Age alignment,
Cohort timing,
Baseline comparison,
Death timing accuracy,
Context.
It was not evidence. It was fuel for a conclusion they’d already decided on.
🧩 And what did it achieve?
The rollout didn’t stop.
The public wasn’t informed — they were inflamed.
The dataset wasn’t validated — it was weaponised.
And in February 2024, Te Whatu Ora shut down all public reporting of dose count and outcomes.
We don’t know if that decision was directly linked to the breach — but if you’re looking for causal relationships, it’s one of the few you could actually make a case for.
🔍 Why I won’t play their game
This isn’t about personalities.
It’s about methods.
If your dataset:
Isn’t complete,
Isn’t validated,
Can’t align to cohort timelines or age-based outcomes,
And is used to claim mass murder without margin of error…
…it’s not analysis. It’s narrative.
I don’t worship Barry for “leaking the data.”
Because he didn’t leak all the data.
He leaked a subset that wasn’t capable of showing what he thought it did.
I don’t worship Liz Gunn.
Because 1 million votes and “Whistleblower legislation” don’t make incomplete data true.
And I don’t worship Steve Kirsch.
Because science isn’t about who yells loudest on Substack. It’s about falsifiability, precision, and cohort alignment.
🐾 I stand in the middle. Alone, maybe — but grounded.
I’m not here to win likes from the vaccine loyalists.
I’m not here to cheer for Team Harm, though they’re definitely ahead, according to the data.
I’m here to ask, month after month:
What does the data literally say?
Sometimes, it says things no side wants to hear.
Sometimes, it says things both sides twist into something else.
But I’ll still be here — reading it, structuring it, and posting what it shows.
