@nicknicknicknick Seems very interesting. Why only 3 stars though?
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Scott started reading Harriet Tubman : Live in Concert by Bob the Drag Queen
Sally Strange replied to nicknicknicknick's status
Content warning lewd; violence

nicknicknicknick reviewed The Shortest History of China by Linda Jaivin (The Shortest History)
The Shortest History of China
3 stars
Content warning lewd; violence
1) "Northerners prefer wheat and southerners rice, but not always; some Chinese never touch chili, while others can't cook without it. Beijingers complain that Shanghainese are mercantile and petty; Shanghainese snipe back that Beijingers are bighearted but crude. All stereotypes fall apart in the face of Chinese diversity. The citizenry of the PRC includes subsistence farmers and jet-setting billionaires, Buddhist monks and nightclub owners, passionate feminists and steely patriarchs, avant-garde artists and aerospace engineers, yak herders and film animators, pro-democracy activists and loyal Communists. They may live in towering apartment blocks, courtyard houses built to a two-thousand-year-old design, European-style villas, longhouses, stilt houses, yurts, or even modified caves. They may be fans of Peking opera, Western opera, punk, throat-singing, Cantopop, chess, video games, Korean soap operas, calligraphy, photography, ballroom dancing, fan dancing, all or none of the above."
2) "Whereas Confucians were obsessed with the correct course of action in any situation, Daoists preached wúwéi 無為, 'inaction'—flowing like water does in nature. Followers of Confucius yearned to serve a ruler; Daoists were famously uninterested in taking part in government. Their followers developed a diverse set of rituals and disciplines, ranging from meditation, alchemy, and energetic healing to the pursuit of immortality through sexual practices (such as non-ejaculation for men) and the consumption of potions. Daoists have irritated straight-laced Confucians for millennia."
3) "Qin Shihuang, wary of the subversive power of a strong landed gentry, instead carved his realm into thirty-six (later forty-eight) administrative regions. A joint civil and military bureaucracy were put in charge of these, reporting directly to him—the Qin was thus the first unified and centralized Chinese state. Its capital was at Cháng'ān (near present-day Xī'ān, in the Northeast). He introduced a single currency, round copper coins with a square hole in the middle—a template used until 1911. He also unified measurements for length and volume, and even standardized the width of cart axles—an inspired solution to the danger and inconvenience posed by unsurfaced roads furrowed by differently spaced wheel ruts."
4) "The Sui put all land under dynastic control and distributed it to the people on the basis of their ability to cultivate it. Because the land reverted to the ruling house for reassignment after its cultivator's death, this prevented the emergence of powerful landowning families that could challenge the central authority. Agricultural production revived, and the economy grew. But the Sui, after that promising start, began its decline under its second emperor, who was fatally fond of both luxury and ill-considered military campaigns. Along the densely populated reaches of the Yellow River, land reclamation and deforestation caused by farming led to worsening floods. A catastrophic flood in the late Sui, earning the Yellow River the moniker 'China's sorrow,' appeared to augur the dynasty's loss of the Mandate of Heaven."
5) "Having served as a local official for several decades, Wang Anshi had observed how the accumulation of vast estates by powerful families allowed them to exploit those who tilled the land. He didn't see why the rich deserved to own so much more than anyone else. Besides, the wealthy, he observed, were adept at tax avoidance, allowing the burden of taxation to fall on those least able to support it. Confucian moral example and rites weren't going to solve these problems. Only Legalist methods would bring about social justice and equality."
6) "The detailed realism that characterizes the Qingming shanghe tu and many other paintings of the time reflected the spirit of reason that so obsessed the neo-Confucians. Yet other artists of the Song would take painting to new heights of intuitive expression, creating a fresh pictorial language in the process. Paradoxically, this also owed something to neo-Confucianism, which considered displays of technical virtuosity unworthy of the Confucian gentleman. This ideal achieved its highest expression in Song landscape, or 'mountain and water' paintings, shānshuǐ huà 山水畫. Combining the arts of painting, poetry, and calligraphy—often literally, with poems calligraphed onto the paintings themselves—such paintings aimed to capture the poetic essence of a scene, exploring the tension between abstraction or emptiness, xū 虛 (the void), and representation, shí 實 (what is real or solid). A poet of the Song dynasty, Sū Dōngpō (1037–1101), wrote of the Tang poet-painter Wáng Wéi (699–759), whose work is considered a forerunner: 'Savor his poetry, and there is a painting in each poem; look carefully at his paintings, and each one contains a poem.'"
7) "Another great Ming novel was the sixteenth-century Journey to the West, aka Monkey, which fictionalizes and injects supernatural elements into the story of the Tang monk Xuanzang's journey to India to bring back original Buddhist texts. In the novel, a talking pig and the mischievous, havoc-making Sūn Wùkōng—better known in English as the Monkey King—accompany the monk. The Monkey King employs 'magic weapons,' fǎbǎo 法寶, to battle various 'cow demons and snake spirits,' including the fearsome White-Bone Demon. The novel is an entertaining blend of mythology, Daoism, Buddhism, Confucianism, and satire. The inspiration for almost thirty films, television series, comics, anime, and operas, it is one of China's most successful cultural exports."
8) "In June 1917, before a new president could put the pieces of Chinese democracy together again, Qing loyalist general Zhāng Xūn (1854–1923), nicknamed 'the Pigtailed General' for his queue, staged a coup. He put the eleven-year-old Puyi, who'd been living in the inner court of the Forbidden City all this time, back on the throne. The republican air force dropped three bombs on the Forbidden City. Only one exploded, injuring one of Puyi's palanquin bearers. After twelve days, Puyi abdicated a second time but was still allowed to live in the palace. The Pigtailed General sought asylum in the Dutch legation. A new, hapless president was installed as the country slid further into chaos and division, and warlords (men with a territorial base and an army to defend it) carved it into virtual fiefdoms. Some warlords were Yuan loyalists. Others were gangsters or opium runners. One, a Christian, baptized his troops with a hose. Another promoted a political program dizzily combining 'militarism, nationalism, anarchism, democracy, capitalism, communism, individualism, imperialism, universalism, paternalism and utopianism.'"
9) "Puyi, the last emperor, had lived an odd and useless life, surrounded from childhood by eunuchs and other members of his defunct imperial court. He had never even been outside the Forbidden City when, in 1922, he cut his queue and, inspired by the example of his Oxford-trained tutor, Reginald Johnston, decided to escape to England. When Johnston refused to call him a cab, he gave up on the idea."
10) "In 1968, following intense factional fighting in Guangzhou, hundreds of bloated corpses, many trussed and bearing gunshot wounds or signs of torture, floated into Hong Kong waters from the Pearl River, further hardening anti-Communist sentiment in a territory that had for decades served as a place, as the Hong Kong journalist Lee Yee later put it, for 'fleeing the Qin.' By the end of 1968, Mao's enemies in the CPC were silenced. Intellectuals who survived the purges were sent off for 'reeducation.' The Red Guards had served their purpose. It was now necessary, Mao said, for urban youth to go to the countryside and 'learn from the poor and middle-level peasants.' In 1969, the CPC declared the Cultural Revolution over."
Sally Strange started reading Blood Trials by N. E. Davenport (The Blood Gift Duology, #1)
This is kind of like a mash-up of Hunger Games, GI Jane, and Tananarive Due's African Immortals series (because of its obsession with blood). The protagonist is a little dumb and a lot violent, but you have to excuse her because she's Black and female and blessed with God-granted superpowers in a society that hates all three of these things. Also her grandfather, who raised her, recently died, and she just discovered that he was actually murdered. Plus, what with the superpowers that make her faster and stronger and quicker to heal than the average bear, if she weren't a little ignorant and slow to catch on, she would have zero weaknesses and there wouldn't be much of a story.
Sally Strange finished reading The Rose Rent by Edith Pargeter
Still good, although the narrator, or perhaps it's the fault of the recording, had a tinny, grating timbre.
One thing I really enjoy about these Brother Cadfael mysteries is that mostly the perpetrators don't go to jail. They get exiled or killed through their own machinations, or their crime had extenuating circumstances and Cadfael helps them get away with it.

New issue of @UncannyMagazine is here and on its way to subscribers! https://weightlessbooks.com/uncanny-magazine-issue-64/
Sally Strange wants to read The Adventures of Mary Darling by Pat Murphy
Based on this rave review from Cory Doctorow, I am putting this high up on my to-read list!
pluralistic.net/2025/05/06/nevereverland/#lesser-ormond-street
Sally Strange wants to read Sky Full of Elephants by Cebo Campbell

Sky Full of Elephants by Cebo Campbell
In a world without white people, what does it mean to be Black?
One day, a cataclysmic event occurs: all …

otter replied to Sally Strange's status
@SallyStrange she deserves better behavior than smidges and tads tbh
Sally Strange replied to otter's status
@thief@weirder.earth Certainly. The wording reflects my opinion of the effort that would have been involved in not being so racist as to drive her away completely.
Sally Strange commented on The Disordered Cosmos by Chanda Prescod-Weinstein
This chapter about melanin is so fascinating and surprising. Melanin is a conductor, that is also an insulator, depending on the presence or absence of water? Melanin could be a building material? Melanin could be the key to constructing materials for bioelectric implants? Awesome stuff, and Dr. Prescod-Weinstein is right to consistently rail against the exclusion of Black people from academia and physics in particular. Damn, Fedi, she was here and then she left. Imagine how much cooler this place would be if us white folks had been a smidge more hesitant to do microaggressions, a tad less disbelieving of the direct first-hand reports of racism and white supremacy from her and others like her.

Sasu reviewed How Emotions Are Made by Lisa Feldman Barrett
Fascinating Theory with Practical Applications
5 stars
Even though it's been a few years since I listened to this one, it often resurfaces in my thoughts. In short, the theory of #emotion presented in this book is powerful because of its flexibility and its ability to explain: - how #reframing is even possible - how there can be such large #emotional differences between cultural groups - how it could have been possible that the way humans construct emotion has changed over the course of #history
Sally Strange started reading The Chromatic Fantasy by H.A

Soh Kam Yung reviewed Why Machines Learn by Anil Ananthaswamy
A mathematical look at how machines learn and make decisions.
4 stars
A fascinating book that looks at the history of Machine Learning (ML) to show how we arrive at the machine learning models we have today that drive applications like ChatGPT and others. Mathematics involving algebra, vectors, matrices, and so on feature in the book. By going through the maths, the reader gets an appreciation of how ML system go about the task of learning to distinguish between inputs to provide the (hopefully) correct output.
The book starts with the earliest type of ML, the perceptron, which can learn to separate data into categories and started the initial hype over learning machines. The maths are also provided to show how, by adjusting the weights assigned to its testing input, the machine discovers the correct weights which can allow it to categorize other inputs.
Other chapters then cover other ways to train a machine to categorize its input is shown, based on …
A fascinating book that looks at the history of Machine Learning (ML) to show how we arrive at the machine learning models we have today that drive applications like ChatGPT and others. Mathematics involving algebra, vectors, matrices, and so on feature in the book. By going through the maths, the reader gets an appreciation of how ML system go about the task of learning to distinguish between inputs to provide the (hopefully) correct output.
The book starts with the earliest type of ML, the perceptron, which can learn to separate data into categories and started the initial hype over learning machines. The maths are also provided to show how, by adjusting the weights assigned to its testing input, the machine discovers the correct weights which can allow it to categorize other inputs.
Other chapters then cover other ways to train a machine to categorize its input is shown, based on Bayes Theorem and nearest neighbour. They have their advantages and disadvantages: choosing the right (or wrong) way to train a machine will have an impact on how well the machine can categories its data.
Matrix manipulation, eigenvalues and eigenvectors are then introduced. When there are many input parameters, it can be hard to categorize them based on all the factors. By using eigenvalues and eigenvectors, it is possible to discover which factors cause the most variation among the data, and thus categorize them. And, in an interesting reversal, it is also possible to manipulate the input by putting them into more categories, which can reveal patterns that can then be used to categorize the input.
These ML models categorize input data using one level of 'neurons'. The next step would be to introduce a 'hidden layer' of neurons that can be used to combine the incoming data in many ways, which provides new ways to manipulate the data for categorization. This would provide a boost in the abilities of machines to recognize input data.
Lastly, the book catches up to current day ML models, which feature a huge increase in the number of hidden layers and weights used to manipulate input data. The book then points out that this huge increase has caused the theory of how machines learn to fall behind: the machines now exhibit abilities that theory cannot account for. The ability of such machines to pick out patterns in data through self-learning, rather than being pre-fed known data, is also an unexpected feature that current ML theories cannot account for.
These unaccounted features of current day ML systems are a probable cause of concern. So too is the concern over the kind of data being pre-fed to the systems: data that comes with various biases that only cause the system to make yet more biased decisions. Until we know better how these systems behave, it would be best to treat their outputs with caution.