The golden age of language was a few thousand years ago 46%

By Sujata Gupta27%

7/23/2026, 6:01:34 PM

BS Summary: This article contains 22 faulty reasoning types, including Negativity Bias, Confirmation Bias, and Availability Heuristic, with Overconfidence Bias as the most egregious example at 13.7% saturation with 95 hits. Analysis detected 766 faulty-reasoning hits from 694 analyzed words, generating a BS Score of 47.8% and a BS Rank of 46% (12,007 of 21,887 articles). This article is better (less manipulative) than 54.90% of the article peer group.

Some 7,500 languages are spoken or signed around the world today. 
Though that might sound like aA lot, the number could have been up to 10 times higher a few thousand years ago, researchers report July 23 in Science. 
“That was the golden age of linguistic diversity,” says Claire Bowern, a linguist at Yale University. 
The finding comes as scientists are racing to document, or ideally preserve, languages nearing extinction. 
Half of today’s languages are now endangered, and roughly four languages disappear every year. 
Linguists and cognitive scientists have long sought to identify features of language, whether unique to a given culture or universal, to generate theories about how humans reason about the world. 
Any underestimate of linguistic diversity would mean those theories are missing a lot of what is possible. 
But ancient languages are challenging to study. 
They don’t leave a fossil record. 
And writing emerged relatively recently  in the past 6,000 years  and among only a subset of languages. 
So Bowern’s team developed a model to try to quantify what might have happened to language diversity over the past several thousand years, including pinning down when it started to shrink. 
The team first looked at more than 170 contemporary hunting and gathering groups worldwide as a proxy for past populations. 
Though such groups have changed across time, many key aspects of their social structure have remained stable, research elsewhere suggests. 
Chiefly, foraging groups vary in size but typically include several hundred people to more than a thousand. 
And, broadly speaking, each group speaks a single language. 
That 1–1 ratio of languages to foraging groups likely dominated before farming emerged some 12,000 years ago, the team concluded. 
With a global population of 4.4 million to 7 million people at that time, the total number of languages then was probably between 4,500 and 6,200. 
This gave the team both a starting point for the model and an ending point, based on present day numbers. 
“At the start of our modeling 12,000 years ago, we have roughly 5,000 languages to 6 million people. 
At the end point, we have 7,500 languages for 8 billion people,” Bowern says. 
The puzzle became what happened in between. 
The pivot to agriculture would have changed quite a lot. 
Abundant food enabled population sizes to balloon, as well as the number of people speaking any given language. 
Though language diversity would increase, it wouldn’t keep pace with population growth, Bowern says. 
Assume that a growing population speaking the same language split into two. 
Based on what’s known about language evolution, it would take 500 to 1,000 years for their languages to become distinct. 
So the team simulated multiple scenarios that included the known data points and existing assumptions, such as varying population estimates based on the existence or absence of disease. 
Under every scenario, language diversity peaked at between 20,000 and 75,000 languages some 1,000 to 3,000 years ago. 
Then that diversity rapidly dropped off. 
The timing suggests that agricultural intensification and the rise of nation-states, such as the Roman and Greek empires, led to catastrophic language loss. 
Bowern’s team now hopes to zoom in to understand how linguistic diversity changes over time on a regional scale. 
The finding is likely to inform, but not settle, ongoing debates over the landscape of languages across time. 
It goes against a popular theory that puts linguistic diversity at its highest before the dawn of agriculture, some 12,000 years ago, as well as another theory that pegs today’s language extinction crisis to the rise of colonialism just 500 years ago. 
It opens a new perspective, says Chiara Barbieri, a geneticist studying language diversity at the University of Cagliari in Sardinia, Italy, who finds the idea that so many languages may have been lost to time mind-boggling. 
There is tremendous variation even among the remaining languages today, from the absence or presence of gendered nouns, articles, clicks to communicate or tones that convey meaning nonnative speakers can’t even hear. 
“Are there people who developed languages with some traits that we can barely imagine now?” 
she wonders. 
“It makes your head spin.” 
Article reasoning-pattern comparisonThis article: 8.9%Sujata Gupta: 2.2%Science News: 2.6%Confirmation Bias8.9%This article: 4.5%Sujata Gupta: 2.7%Science News: 1.1%Anchoring Bias4.5%This article: 8.6%Sujata Gupta: 2.7%Science News: 2.6%Availability Heuristic8.6%This article: 4.2%Sujata Gupta: 2.5%Science News: 1.5%Representativeness Heuristic4.2%This article: 2.4%Sujata Gupta: 0.4%Science News: 0.4%Hindsight Bias2.4%This article: 13.7%Sujata Gupta: 3.4%Science News: 2.3%Overconfidence Bias13.7%This article: 3.3%Sujata Gupta: 2.6%Science News: 4.1%Framing Effect3.3%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.3%Loss Aversion0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.4%Status Quo Bias0.0%This article: 2.9%Sujata Gupta: 0.5%Science News: 0.2%Sunk Cost Effect2.9%This article: 3.7%Sujata Gupta: 3.5%Science News: 4.0%Optimism Bias3.7%This article: 0.0%Sujata Gupta: 0.0%Science News: 1.5%Pessimism Bias0.0%This article: 11.0%Sujata Gupta: 3.8%Science News: 4.1%Negativity Bias11.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.4%Self-Serving Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.5%Fundamental Attribution Error0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%Actor-Observer Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%In-Group Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 1.3%Halo Effect0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Horn Effect0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 1.0%Recency Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.3%Primacy Effect0.0%This article: 2.6%Sujata Gupta: 0.4%Science News: 0.1%Blind-Spot Bias2.6%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%Ad Hominem0.0%This article: 0.0%Sujata Gupta: 1.0%Science News: 0.2%Straw Man0.0%This article: 2.9%Sujata Gupta: 0.5%Science News: 4.4%Appeal to Authority2.9%This article: 8.1%Sujata Gupta: 2.6%Science News: 1.2%False Dilemma8.1%This article: 0.0%Sujata Gupta: 0.3%Science News: 1.1%Slippery Slope0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%Circular Reasoning0.0%This article: 6.6%Sujata Gupta: 3.7%Science News: 3.9%Hasty Generalization6.6%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Red Herring0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.3%Bandwagon0.0%This article: 5.9%Sujata Gupta: 1.1%Science News: 2.5%Appeal to Emotion5.9%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.5%Begging the Question0.0%This article: 7.3%Sujata Gupta: 1.7%Science News: 2.5%Post Hoc (False Cause)7.3%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Tu Quoque0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.5%Burden of Proof0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.3%Appeal to Nature0.0%This article: 4.6%Sujata Gupta: 0.7%Science News: 0.3%Composition/Division4.6%This article: 0.3%Sujata Gupta: 0.0%Science News: 1.2%Anecdotal0.3%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.1%No True Scotsman0.0%This article: 2.4%Sujata Gupta: 0.4%Science News: 1.6%Ambiguity (Equivocation)2.4%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.2%Middle Ground0.0%This article: 2.2%Sujata Gupta: 1.0%Science News: 0.1%Personal Incredulity2.2%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Special Pleading0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Genetic Fallacy0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.7%Unattributed Quote0.0%This article: 2.6%Sujata Gupta: 1.2%Science News: 0.6%Quote-first Misdirection2.6%This article: 1.6%Sujata Gupta: 0.3%Science News: 2.6%Biased Writer Voice1.6%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.9%Indoctrination0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Sujata Gupta: 0.0%Science News: 0.4%Attempt to Sell a Product or S…0.0%

694 words analyzed.

Speakers

2speakers17%attributed speech576writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageClaire Bowern • 16 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageClaire Bowern • 18 words • 100.0% coverageClaire Bowern • 14 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageClaire Bowern • 14 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 42 words • 0.0% coverageChiara Barbieri • 36 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageChiara Barbieri • 15 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageChiara Barbieri • 5 words • 0.0% coverage
Selected voice

Chiara Barbieri

100%flagged-word coverage
56 attributed words47% of attributed speech74% writer coverage
0%2.5%5.0%Biased Writer Voice-1.9 ptsWriter: 1.9%Chiara Barbieri: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.