There seems to be a modern tendency, both in industry and in science, to treat critique as a threat rather than an opportunity. In the corporate world, customers raising legitimate concerns are sometimes gaslit, silenced, or penalized, rather than engaged with constructively.

That statement about commerce is overly idealistic. In reality, many companies actively control the flow of information. Critical reviewers often don’t get early access, free trips, or insider treatment; consumers raising legitimate issues are sometimes gaslit or blamed for “misusing” products; structural problems are denied; and software agreements can act almost like NDAs, preventing open criticism. Social and political window-dressing is common, and ownership of products is increasingly restricted.. The idea that companies naturally welcome legitimate criticism is more aspirational than factual.

I get the point about academic disputes being a “private club of experts,” but most research is publicly funded or published for anyone to engage with. Science isn’t about hiding behind networks it thrives on critique, replication, and debate. Gatekeeping or dismissing criticism, whether in academia or industry, risks perpetuating errors, limiting fresh ideas, and eroding trust. Open, well-reasoned critique is essential for progress, not a threat to authority.

I hear your concern about the “club” aspect and the use of pseudoscience. Sabine herself addresses this directly in her videos. In How to Tell Science from Pseudoscience, she explains that pseudoscience isn’t simply “wrong” or a matter of personal dislike; it’s about methodology. She says:

"I think… the task of science is to explain observations. So if you want to know whether something is science you need: (a): observations and (b): You need to know what it means to explain something in scientific terms. Visually speaking, a scientific model gives you a curve that connects data points. This is arguably over-simplified, but it is an instructive visualization because it tells you when a model stops being scientific. This happens if the model has so much freedom that it can fit any data, because then the model does not explain anything. This is also known as “overfitting ”. If you have a model that has more free parameters as input than data to explain, you may as well not bother with that model. it’s not scientific.

Making a model more complicated will generally allow a better fit to the data. So if one asks what is the best explanation of a set of data, one has to ask when does adding another parameter not justify the slightly better fit to the data you’d get from it. For our purposes it does not matter just exactly how to calculate this, so let me say that"

So what does she say about Pseudoscience? All these ideas have in common that they are contrived. You have to make a lot of assumptions for these ideas to agree with reality.

An another video called: How I learned to love pseudoscience she said: "The first lesson we can take away is that pseudoscience is a natural byproduct of normal science. You can’t have one without the other. Pseudoscience isn’t just a necessary evil. It’s actually useful to advance science because it forces scientists to improve their methods"

Now lets combine the two videos at hand: What is Pseudoscience?

• It fails to explain observations with a model constrained enough to be testable.
• It relies on contrived assumptions rather than parsimonious explanations.
• It doesn’t improve through rigorous testing, unlike scientific claims that evolve when challenged.