poetry for young people

delightful series of books of poems for young people / got these for a book drive in the office \ gives a background about each work such as the author’s intent and in what context it was written / this makes great gifts by adult fans of shakespeare & lewis carroll for their favorite young people

happy national library week!  apr 9-15

 

the shakespeare code

dr. who shakespeare1

happy birthday, will! / i was binge watching doctor who last night & episode 2/season 3 was “the shakespeare code” which weaves into the plot shakespeare’s lost work “love’s labour’s won” 🙂 \ delightfully funny  / love the one and only bard & the tenth doctor!

romeo & juliet and shakespeare rock

romeo and juliet

i have never seen my favorite highschooler so captured & enthralled by an assigned reading \ the object of literary & artistic enthusiasm is shakespeare’s romeo & juliet (~1594-1595) <to my disappointment, to kill a mockingbird (1960) did not inspire even though i tried to sell it as a story from the point of view of a rebel child & tomboy> \ the chosen project is to illustrate favorite passages from the play / pictured above are two of nine

to supplement the reading assignment, the class watched the 1996 baz luhrman movie version / however, we have at home the far superior 1969 franco zeffirelli version \ and this is where lit&film makes the connection with my favorite teenager / a tragedy about her peers holds immediate appeal \ then there is the kicker which is the ill-fated late message / plus the language of shakespeare <what i’ll call intrinsic shakespeare>  \ her favorite passage is in act 3, scene 5

JULIET 
  Wilt thou be gone? it is not yet near day: 
  It was the nightingale, and not the lark, 
  That pierced the fearful hollow of thine ear; 
  Nightly she sings on yon pomegranate-tree: 
  Believe me, love, it was the nightingale.

ROMEO 
  It was the lark, the herald of the morn, 
  No nightingale. Look, love, what envious streaks 
  Do lace the severing clouds in yonder east. 
  Night’s candles are burnt out, and jocund day 
 Stands tiptoe on the misty mountain tops. 
 I must be gone and live, or stay and die.

surprisingly the beauty of olivia hussey who was fifteen when she took on the role of juliet had quite an impact on my favorite teenager who is also fifteen / she would always ask me “do you think so-and-so <friends, schoolmates> is pretty?” & i would give an honest answer \ this time she declares olivia hussey’s incomparable beauty particularly her green eyes & pretty nose / this made me laugh since this declaration came with a complaint about her “half filipino” nose | how pinoy to complain about one’s nose! 🙂 / it was also not lost on her that olivia hussey was about the same age as the “real” juliet  \ other versions failed the realism test for her not because of the modern take on shakespeare but because of the actors / it must also be the language & historical setting that made my favorite teenager favor the classic zeffirelli film

 

 

 

fun with google books ngram viewer

ngram lbd topics-004

while searching for the next book to read, i stumbled upon google books ngram viewer / it will search key phrases in the corpus of “google books” <books that google has scanned & included in its library> & graph the percentage occurrence of the phrases over a time period which you can specify <as far back as 1800!> \ it will also list down the books that were published in ranges of years for those phrases <this was my purpose> / super awesome tool! you can also use your own dataset

what is n-gram? from wikipedia:

In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sequence of text or speech. The items can be phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus. 

An n-gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of a (n – 1)–order Markov model. n-gram models are now widely used in probability, communication theory, computational linguistics (for instance, statistical natural language processing), computational biology (for instance, biological sequence analysis), and data compression.

here’s the fun part / you can make inferences about how the popularity <or current thinking about> of ideas, subjects, people etc. etc. etc. has changed over time \ pretty much like “trending topics” / here’s what i trended <click images to enlarge>

https://books.google.com/ngrams

ngram people & society1ngram famous people <shakespeare still rocks> | ngram generic people <impact of 1970s women’s liberation movement>

ngram music & sports1

ngram music:  got unrelated books with genre keywords such as blues, bluegrass, rock <but jazz is distinct> | ngram sports <“art of swimming” by benjamin franklin 1810>

ngram concerns & isms1

ngram concerns <heaven & hell have caught up> | ngram isms: find your most hated ism <capitalism rules, at least in books>