It may be helpful to think about LLMs as fiction generation machines (which they basically are), and treat all their output as fictional text, however realistic, rather than "hallucinations".

I often show students this figure and ask, how different is the green distribution (p < 0.05) from the blue distribution (p = 0.10)? Just to raise some awareness that the difference between "statistical significant" and "not significant" is not always that significant...

A scholar possessing a singular vision and a frightening working resilience, he was hounded by Stalin repression machine, exiled, denied academic positions, firewood and food; his work was largely forgotten until 2000s.

So we, who were influenced by him at the rise of DH, keep remembering.

As a gift for a patient reader, a graph showing the cohorts in terms of total print runs. *Graphs are better news

I don't have pre-revolutionary data, so “cohorts” do not necessarily correspond to the true date of the translation of the author into Russian, esp. for “classics”. NA stands for books with no author indicated on a cover/title (folklore, collections). Data source: my dataset bsky.app/profile/masl...

WWII was an obvious bottleneck for printing, including translations. Also to note: the rise in number of translations during the Thaw, the effect persisted until around 1976. And the Thaw indeed left its trace on further circulation of translations.

The data is part of Daria's ongoing research, and she does wonderful things with it. As a teaser, here's Daria's graph showing cosine similarity between journals based on the poets who published there. Huge shoutout to Daria for sharing these data!

The data is published in the Repository of open data on Russian literature and folklore, doi.org/10.31860/ope.... The main table has an entry for every work published, and some info on authors, including party membership. Additional tables list editorial teams and the recipients of literary awards ↓

a superficial similarity is also that both result in tables with asterisks

The database is accompanied by the theoretical framework that provides us with the toneme — a comparative concept that allows us to consistently analyze typologically diverse tonal systems. A sister poster at the same conf with concise presentation of the idea: zenodo.org/records/1481...

thot.huma-num.fr/db/ Interactive maps of languages colored by tonal status, sources for tonal status info, structured descriptions of tonal systems of a few sampled languages, accompanied with texts with detailed tonal markup.

Did I mention these data are very special? The print runs of the editions were well documented throughout the Soviet period, and kept as part of bibliographic records. We have good basis here to estimate total print runs, print run by author, by gender etc.

Of 14367 unique authors 82% has a known gender, 26% have info on birth/death year, and 24.5% have wikidata person ID. It may seem like not much, but authors with known wikidata ID comprise more than 65% of total print runs of the whole period. →

transformed into structured table data. The bibliography is the most comprehensive source on all books for children (fic and non-fic) printed in Soviet Russia and USSR. This year's edition includes a separate table of unique authors. Author data has undergone massive cleanup and disambiguation. →

To all bibliographic data lovers (myself included) — a yearly Christmas update of the “Bibliography of Russian children's book 1918-1984” dataset: doi.org/10.31860/ope.... For those new to the show this dataset is based on the digitized 18-volume printed bibliography by Ivan Startsev →