The figure of the femme fatale is one of the defining literary and artistic motifs of the 19th and early 20th Centuries.
Artists were drawn to historical archetypes of female seduction such as Cleopatra or Lucretia Borgia, characters from Old Testament stories including Salome, Judith and Delilah, or mythical figures such as Circe, Helen of Troy and Medea.
Others were conjured from their male author’s imagination – Prosper Mérimée’s Carmen, Émile Zola’s Nana and Frank Wedekind’s Lulu being some of the most notable.
Her emergence is frequently seen as a response to anxieties arising from profound social change as women pushed for greater economic, political and educational rights, challenging the established patriarchal order.
Middle-class women who sought education were, according to the British psychiatrist Henry Maudsley, likely to damage their reproductive organs, turning them into monstrosities who threatened the survival of the human race. Fear of contagious diseases such as syphilis was another factor, with working-class prostitutes being seen as contemporary femmes fatales who could lure their clients to their doom.
JPL-developed technologies, including VITAL, FINDER, 3D-printing methods, and Voyager spacecraft communications, are featured in the agency’s technology publication.
Published Jan. 31, 2023
When it comes to NASA, most people look to the skies as rockets, rovers, and astronauts push the boundaries of space exploration. But the benefits of going above and beyond can be found here on Earth through products and services born from NASA innovation.
Editor’s Note: Read more, see link below for original item…
This year we are welcoming works from 1927 into the public domain in the United States, including books, periodicals, sheet music, and movies.
Big events of 1927 include the first transatlantic phone call from New York to London, the formation of The Academy of Motion Picture Arts and Sciences, the first successful long distance demonstration of television, the release of the first popular “talkie,” The Jazz Singer, and the first nonstop transatlantic solo airplane flight, from New York to Paris, by Charles Lindbergh.
I was an early adopter of ebooks, in part because of my terrible eyesight, but mostly because I happened to break into reviewing just before the 2001 anthrax attacks.
Fear of contaminated packages increased shipping time for cases of manuscripts from four days to forty. Electronic books (which in those long-ago days were really just doc files) provided instant gratification.
At one point, I even considered ditching paper entirely in favor of electronic formats. In addition to the instant gratification angle, one does not have to worry about ebooks overloading the floors of one’s residence. One can carry a few thousand ebooks in one’s pocket. One can—and for me, this is the killer app—adjust font size. Ebooks are great, and I would defend them to your last breath.
By: Jonathan McMichael, Undergraduate Success Librarian
AI writing can mimic style, but it cannot mimic substance yet. The release of a powerful, free and easy-to-use large language model platform, Open AI’s ChatGPT, raises interesting questions about the future of writing in higher education.
As the Undergraduate Success Librarian, I have a unique perspective on generative AI, like ChatGPT, that I want to share along with some advice for instructors and students on adapting to AI’s presence in higher education.
What is ChatGPT?
How does it work? ChatGPT is an interface that allows you to interact with artificial intelligence through text inputs and responses. The AI on the other side of the interface is a language model called GPT-3. It produces human-like text by parsing and analyzing the massive corpus of text information (large language) it has been trained on to predict what is likely to come next in a string of words. This makes GPT-3 a type of Generative AI because it uses machine learning to generate new content based on a given set of input data. So, when you give ChatGPT a prompt like “describe losing your sock in the dryer in the style of the declaration of independence” it (in simplified terms) identifies relevant data within its large language dataset, notices patterns within that dataset and then generates a set of text that seems most like the things it identified.*