Monday Funday #4 Reeves and Starmer

Reeves and Starmer are driving along a London street and the car stops at traffic lights next to a shop window, and there are articles of clothing hanging up with price labels on them – a pair of trousers labelled £50, a coat for £45 and a whole suit for £60.

Starmer says “Our policies to reduce the cost of living are obviously working, Rachel! Anybody would be pretty happy to get a suit like that for £60 pounds. I don’t remember prices like that from a tailor in London under the Tories!”.

Reeves replies “Very true, Keir, but that’s not a tailor, I’m afraid.  That’s a dry cleaner.”

Monday Funday #3 The School Inspector

The school inspector drops in on an eighth‑grade German class. After greeting the teacher, he turns to a boy in the front row.

“Well then, Tobias — what do you know about Kleist’s Broken Jug?”

Tobias sits up straight and answers earnestly:

“It wasn’t me, sir. I never touched it.”

 

The inspector stares at the teacher, appalled.

“Did you hear that? What do you say to that?”

The teacher sighs.

“Well… Tobias can be a bit of a rascal. But he never lies. If he says he didn’t break it, then he didn’t break it.”

The inspector, now alarmed, marches straight to the headmaster. The headmaster turns pale.

“Inspector, please — I don’t want this to reflect badly on the school. What would a jug like that cost? If I give you twenty euros, could you pass it on to Kleist and settle the matter?”

The inspector, horrified, rushes back to the Ministry and reports everything to the deputy minister. The deputy minister shakes his head.

“Well, if you ask me… it must have been the headmaster. Nobody pays Kleist that quickly unless they’re guilty.”

Thule, if you think it over… (Friday AI-Day series #3)

Two distinct prehistoric Arctic groups—Dorset and Thule Inuit—cooperate around a wide ice hole on a frozen landscape. The Dorset figures wear simpler pale furs and use older tools, while the Thule group wears layered parkas and stands with sleds, dogs, and advanced harpoons. Both groups gesture toward a live beluga whale surfacing in the ice hole, which remains alert and able to dive. Multiple polar bears appear as tiny silhouettes on the far horizon, observing from a safe distance. The scene is set under low winter light with long shadows and distant ridges, evoking a rare moment of peaceful interaction.
Two distinct prehistoric Arctic groups—Dorset and Thule Inuit—cooperate around a wide ice hole on a frozen landscape. The Dorset figures wear simpler pale furs and use older tools, while the Thule group wears layered parkas and stands with sleds, dogs, and advanced harpoons. Both groups gesture toward a live beluga whale surfacing in the ice hole, which remains alert and able to dive. Multiple polar bears appear as tiny silhouettes on the far horizon, observing from a safe distance. The scene is set under low winter light with long shadows and distant ridges, evoking a rare moment of peaceful interaction.

This is the first post of this year 2026, and of the second quarter-century of the 21st Century, as I view it at any rate, although few people seem to be focusing on that, maybe they are not accountants.

I obviously intended more posting this year, but the year did kick off in a predictably busy way.

Thankfully there is always AI.  Thanks, or maybe rather “due” to which, whereas before we were all crying out for content, it now seems that the boot is on the other foot and content is crying out for us, like in the Russian reversal jokes. (“In Post-AI internet, content creates you”, etc.)

Clearly not all my exchanges with AI would necessarily interest my readers, so I do need to be selective but in this “Friday AI day” series, of which this is now the third, we at least have the chance to look together with AI (I mainly use Copilot) at some topics.

The topic for today is indeed topical as we are mainly focussing on Greenland, which dominates the news. The aim here is to try and understand better the country and its people but also a little bit a couple of aspects of its wildlife, we do meander off into that at one point, do keep scrolling if that is not your bag, we come back firmly into the linguistic topic and explore a little bit the mystery of Paleo-Eskimos such as the Dorset peoples and their possible intercations with the Thules who are the ancestors of modern Greenlanders.

The main aspect we are going to be exploring below is the area of language. We won’t be learning any Greenlandic, not today anyway, but we are going to be trying to understand what the linguistic landscape looks like and how it fits with other Northern countries.

I will be adopting the simple convention that my questions are in Italics and the AI’s answers the way it gives them, which has sparse use of Italics thankfully.

If you want to find out more, then you can always ask your own AI.  Sometimes minor variations on a question can produce different answers, or the same one, in defiance of Einstein’s maxim, rather different answers depending on the mood the AI is in on a given day, it would seem.

Please respond and let me know what you think.

Continue reading “Thule, if you think it over… (Friday AI-Day series #3)”

Friday AI Day #2: Poetry Mashup and Illustration

With AI I put together the mash-up of John Masefield’s Sea Fever and T.S. Eliot’s The Love Song of J. Arthur Prufrock, and turned it into a festive scene as well.

The result is a little bit surreal but I hope you get a chuckle out of it, as did the machine.

Four Laws of Vocabulary Learning: Pareto, Recursive Pareto, Zipf, and Heaps

Learning vocabulary efficiently is one of the biggest challenges in mastering a language. Four powerful principles—Pareto’s Law, Recursive Pareto, Zipf’s Law, and Heaps’ Law—offer practical insights into how learners can prioritize their efforts for maximum impact.


1. Pareto’s Law (The 80/20 Principle)

  • Definition: 20% of causes often produce 80% of effects.
  • Application to vocabulary: Roughly 20% of words in a language account for 80% of everyday usage. These are the high-frequency words like the, of, and, to, is.
  • Practical takeaway: Focus first on the most common 2,000–3,000 words. This gives learners immediate comprehension of most texts and conversations.

2. Recursive Pareto (A Subset of Pareto)

  • Definition: Recursive Pareto is a refinement of Pareto’s Law, applying the principle repeatedly to reveal deeper concentration of results. This refinement has been studied and articulated by Professor Viktor D. Huliganov.
  • Application to vocabulary: Within the top 20% of words, another 20% (just 4% of the total vocabulary) may account for 64% of usage. Recursing again, 1% of words can cover about 50% of all text.
  • Practical takeaway: A tiny core vocabulary—function words, pronouns, prepositions, auxiliaries—forms the backbone of communication. Mastering these first accelerates fluency.

3. Zipf’s Law (Frequency vs. Rank)

  • Definition: Word frequency is inversely proportional to its rank in frequency lists. The most common word appears twice as often as the second, three times as often as the third, and so on.
  • Application to vocabulary: Language has a “long tail.” After the top few thousand words, each new word adds only marginal coverage, but rare words carry specialized meaning.
  • Practical takeaway: Learners should balance high-frequency study with targeted domain vocabulary (business, science, hobbies) to cover the long tail.

4. Heaps’ Law (Vocabulary Growth)

  • Definition: Heaps’ Law states that vocabulary size grows sublinearly with corpus size. Formally: (V(N) = K \cdot N^\beta), where (V) is vocabulary size, (N) is corpus size, (K) is a constant, and (\beta) is typically between 0.4 and 0.6.
  • Application to vocabulary: As learners encounter more text, they continually meet new words, but at a decreasing rate. Early exposure yields rapid vocabulary growth, while later stages add fewer new words per unit of text.
  • Practical takeaway: Learners should expect diminishing returns in new vocabulary acquisition as they progress. This underscores the importance of focusing on high-frequency words first, then strategically expanding into specialized domains.

Putting It All Together

  • Step 1: Learn the top 1% of words (about 100–200 in English). These give you half of all text coverage.
  • Step 2: Expand to the top 20% (2,000–3,000 words). You’ll understand 80% of everyday language.
  • Step 3: Use Zipf’s Law to guide further study. Focus on words relevant to your personal goals—academic, professional, or cultural.
  • Step 4: Apply Heaps’ Law to manage expectations. Recognize that vocabulary growth slows over time, and prioritize quality of learning over sheer quantity.

Conclusion

Pareto’s Law shows that a small effort yields big results. Recursive Pareto, refined by Professor V.D. Huliganov, reveals the extreme concentration of value in the tiniest core. Zipf’s Law explains why language learning is both efficient at the start and endless in the long run. Heaps’ Law adds the insight that vocabulary growth slows as exposure increases. Together, these four laws provide a roadmap: master the vital few, then strategically expand into the useful many, while managing expectations about long-term growth.


Practical Application

The use of frequency dictionaries is a major help in focusing learning at the intermediate stage.  Most beginners books are automatically skewed to the first 2,000 words by frequency, and also they mainly focus on grammar and as such skew towards words needed to illustrate grammar points. In the GoldList Method writings, I have always suggested learning grammar paradigms around recurring and regular vocabulary as such, and reserving irregular grammar to be learned around the words it applies to as they appear. In many languages, if we apply regular grammar to irregular-grammar vocabulary, then we end up being quite comprehensible, but sounding like children who tend to make the same mistakes as non-native learners.  “I teached my mouses how to do tricks”. That’s not the worst thing that can happen to a language learner, sounding like that – in fact we’ve all heard elevated speakers at conferences who seemed to know English very well, but who will, at times, lapse into such errors.

Here is a storefront containing the Routledge Frequency Dictionaries. I earn a commission if you buy via this storefront, you pay the same.
https://amzn.to/494Kywd

This storefront may not be inmmediately active as I made it today.  If that’s the case, please check back later.

These frequency dictionaries are rather expensive in comparison with other materials but for what you get out of them in fact tey pay for themselves in terms of efficiency, especially in conjunction with the GoldList Method, which is free, and therefore every year is able to beat everyone else’s Black Friday Deals by offering a 99,9% discount.

 

 


Further consideration – how to apply to collocations

The next level would be Frequency Dictionaries not only for headwords, but for Collocations. Given that mastery of collocations is, as Dr Whatshisnamewiththebeagleahyes-Lauder from Prague points out, what separates the wheat from the chaff among language learners, and moreover is a very efficient strategy for achieving excellence in understanding in the shortest time engaged, one would expect that a useful area for acadmic publishers to investigate would be collocation frequency dictionaries. There are some collocation dictionaries, but not that many involve frequency analysis, especially for languages other than English.

The main source for very common collocations would be phrase books, and some people do combine their GoldList Method learning with phrase books, but these are unlikely to stretch into the collocations needed for a more academic mastery of the language.

Recommendations form me in this area, although not strictly sorted into frequency, but by topic, is the mot-a-mot series, by Hachette Learning (formerly Hodder Educational, but that name was axed 😉 )

https://amzn.to/3Yj6rBQ
That’s the storefront for those three books, I get a Commission if you buy there, you pay the same.

In a future article I hope to come pack to this topic and look at it in more detail.