We are no longer living in the systemically racist days of Lord Byron, when he wrote Canto XVII of Don Juan:

There is a commonplace book argument,

Which glibly glides from every vulgar tongue

When any dare a new light to present:

‘If you are right, then everybody’s wrong.’

Suppose the converse of this precedent

So often urged, so loudly and so long:

‘If you are wrong, then everybody’s right.’

Was ever everybody yet so quite?

Of course, when Byron wrote this canto, he was referring to privileged male cis-heteronormative patriarchal white supremacist deniers and contrarians like Pythagoras, Socrates, Martin Luther, Galileo, John Locke, and even Byron himself, who challenged the consensus.

In our times, science is a force to be believed in, and deniers and contrarians are evil. That is why research articles such as one entitled, Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature, which was done by virtuous Cornellians, are welcomed. We no longer have to wait for everybody to agree that humans are the major cause of climate change. We can be certain when 99% or even 99.9% of the scientific consensus agree.  Indeed, in the report, Lynas (2021) et al. conclude:

Our finding is that the broadly-defined scientific consensus likely far exceeds 99% regarding the role of anthropogenic GHG emissions in modern climate change, and may even be as high as 99.9%.

This conclusion is so secure, that when doing their research, Lynas et al. (2021) did not even have to read the entire papers they cite. Their report is based on their rigorous scientific reading of 3000 abstracts. By reading only the abstracts of this randomized subset of the 88125 climate-related papers published since 2012, they could publish the paper while there was still time to save Martha’s Vineyard from becoming submerged due to sea level rise.

The analysis used in this study is easy for every layperson to understand. Lynas et al. (2021) searched the Web of Science for English language ‘articles’ published between 2012 and November 2020 with the keywords ‘climate change’, ‘global climate change’ and ‘global warming’. By adding the term ‘global climate change,’ they improved on previous studies.

Lynas et al. (2021) found 4 abstracts out of the 3000 randomly selected abstracts that were implicitly or explicitly skeptical. When they rated the abstracts, only the title and abstract were visible. To make the study even more rigorous, information about authors, date and journal were hidden.

By dividing 4 by 3000 they got 0.00133333333 or 0.133333333% of the 3000 randomly selected abstracts were implicitly or explicitly skeptical. This means that 99.866666667% of the 3000 randomly selected abstracts agreed with the consensus. Lynas et al. (2021) give the 95% confidence limits for this proportion to be 99.62%–99.96%, and proclaim that it is likely that the proportion of climate papers that favor the consensus is at least 99.62%.

To achieve even great rigor, Lynas et al. (2021) programmed R to calculate the observed data at the 99.999% confidence level. Then they found that the confidence interval was 99.212%–99.996%, showing that it is virtually certain that the proportion of climate papers that do not dispute that the consensus is above 99.212%.

Lynas et al. (2021) acknowledge that they did not perform an author elicitation survey asking authors to carry out a self-rating of their papers. Again, this was necessary so that the paper would be published before Martha’s Vineyard became submerged due to sea level rise.

Consequently, Lynas et al. (2021) concluded with high statistical confidence that the scientific consensus on human-caused contemporary climate change—expressed as a proportion of the total publications—exceeds 99% in the peer reviewed scientific literature.

We at the Babbling Bear applaud the creative use of social science techniques to make a conclusion that heretofore would have needed the racist techniques of the natural sciences that are based on objective, rational linear thinking with its goal of analyzing observations and experiments to elucidate cause and effect relationships.