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When Affirmative Action Was White

  by Ira Katznelson


(about 231 pages)
57,752
total words
of all the books in our library
31.25%
vividness
of all the books in our library
4.95%
passive voice
of all the books in our library
2.87%
all adverbs
of all the books in our library
1.44%
ly-adverbs
of all the books in our library
1.43%
non-ly-adverbs
of all the books in our library

clippings from this book

We’ve analyzed hundreds of millions of words, from thousands of different authors, training our linguistic models to recognize the most vivid words in the English language… the words that create the most intense sensory experiences: colors, textures, sounds, flavors, and aromas.

Based on our analysis, we’ve scanned through the pages of this book to find the two pages at the extremes, both the most-passive and the most-vivid pages, so that you can compare them side-by-side and see the difference:

MOST PASSIVE PAGE
MOST VIVID PAGE
were unjust, they, too, must be included in the remedies. When national policy kept out farmworkers and maids, the injury was not limited to African Americans. Nor should the remedy be. On this understanding it is important to identify recipients of affirmative compensation who have a direct relationship to the harm being remedied. This does not mean that they had to experience a specific act of discrimination directly. To qualify, however, it needs to be shown how discriminatory institutions, decisions, actions, and practices have negatively affected their circumstances. This approach does not limit remedies to individuals who have faced injustice directly, one at a time; neither does it justify remedies for African Americans as an exclusive group that has shared in a history of racism except when the harm, as in military segregation, was created with unambiguously racist categories. Popular and political support, in short, as well as judicial legitimacy, will depend on the clarity and persuasiveness of the association between harms and remedies. One of two approaches is possible. In the first, a closely targeted program of corrective justice would search for identifiable individuals who have been harmed—even at the distance of one or two generations—by the pattern of exclusions and local administration this book has documented. This policy could yield both tangible and symbolic compensation. As examples: * For the lag in entering the Social Security system, the excluded could be identified and they, or their heirs, could be offered one-time grants that would have to be crossed the Alabama River, assaulting them with tear gas, whips, and clubs, and trampling them with horses until they retreated to town over the Edmund Pettus Bridge. With seventeen hospitalized at Good Samaritan Hospital for fractured legs, arms, ribs, and heads, and another forty treated for the effects of tear gas and minor wounds in the emergency room, “some 200 troopers and possemen with riot guns, pistols, tear gas guns and nightsticks later chased all the Negro residents of the Browns Chapel Methodist Church area into their apartments and houses.” At the mass meeting of some seven hundred people who gathered in Browns Chapel, Hosea Williams—who, in the absence of Dr. King had led the march alongside African American civil rights leader John Lewis—quietly observed: “I had fought in World War II, and I once was captured by the German army, and I want to tell you the Germans never were as inhuman as the state troopers of Alabama.” Played out on national television, the piercing cries of terror and the visible brutality of gas-masked troopers riveted the American public. Much of the nation watched news reports of the second, more multiracial march led by Dr. King two days later. Protected by federal marshals but barred by an injunction from moving on past the Pettus Bridge, the marchers stopped in front of a double line of Alabama troopers and sang the civil rights movement’s leading freedom hymn. “If you have never heard 2,000 Negroes and whites sing

emotional story arc

Click anywhere on the chart to see the most significant emotional words — both positive & negative — from the corresponding section of the text…
This chart visualizes the the shifting emotional balance for the arc of this story, based on the emotional strength of the words in the prose, using techniques pioneered by the UVM Computational Story Lab. To create this story arc, we divided the complete manuscript text into 50 equal-sized chunks, each with 1155.04 words, and then we scored each section by counting the number of strongly-emotional words, both positive and negative. The bars in the chart move downward whenever there’s conflict and sadness, and they move upward when conflicts are resolved, or when the characters are happy and content. The size of each bar represents the positive or negative word-count of that section.

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