Big Data: A Revolution That Will Transform How We Live, Work and Think 本书作者Viktor Mayer-Schonberger签名版 限量独家销售 azw3 电子书 免费 下载地址

　　A revelatory exploration of the hottest trend in technology

and the dramatic impact it will have on the economy, science, and

society at large.

Which paint color is most

likely to tell you that a used car is in good shape? How can

officials identify the most dangerous New York City manholes before

they explode? And how did Google searches predict the spread of the

H1N1 flu outbreak?

The key to answering these

questions, and many more, is big data. “Big data” refers to our

burgeoning ability to crunch vast collections of information,

analyze it instantly, and draw sometimes profoundly surprising

conclusions from it. This emerging science can translate myriad

phenomena—from the price of airline tickets to the text of millions

of books—into searchable form, and uses our increasing computing

power to unearth epiphanies that we never could have seen before. A

revolution on par with the Internet or perhaps even the printing

press, big data will change the way we think about business,

health, politics, education, and innovation in the years to come.

It also poses fresh threats, from the inevitable end of privacy as

we know it to the prospect of being penalized for things we haven’t

even done yet, based on big data’s ability to predict our future

behavior.

In this brilliantly clear,

often surprising work, two leading experts explain what big data

is, how it will change our lives, and what we can do to protect

ourselves from its hazards. Big Data is the first big book about

the next big thing.

1  NOW

2  MORE

3  MESSY

4  CORRELATION

5  DATAFICATION

6  VALUE

7  IMPLICATIONS

8  RISKS

9  CONTROL

10  NEXT

Notes

Bibliography

Acknowledgments

Index

　　VIKTOR MAYER-SCH?NBERGER is Professor of Internet Governance

and Regulation at the Oxford Internet Institute, Oxford University.

A widely recognized authority on big data, he is the author of over

a hundred articles and eight books, of which the most recent is

Delete: The Virtue of Forgetting in the Digital Age. He is on the

advisory boards of corporations and organizations around the world,

including Microsoft and the World Economic Forum.

　　KENNETH CUKIER is the Data

Editor of the Economist and a prominent commentator on developments

in big data. His writings on business and economics have appeared

in Foreign Affairs, the New York Times, the Financial Times, and

elsewhere. 

暂无出版社相关信息，正在全力查找中！

　　1

　　NOW

　　IN 2009 A NEW FLU virus was discovered. Combining elements of the

viruses that cause bird flu and swine flu, this new strain, dubbed

H1N1, spread quickly. Within weeks, public health agencies around

the world feared a terrible pandemic was under way. Some

commentators warned of an outbreak on the scale of the 1918 Spanish

flu that had infected half a billion people and killed tens of

millions. Worse, no vaccine against the new virus was readily

available. The only hope public health authorities had was to slow

its spread. But to do that, they needed to know where it already

was.

   In the United States, the Centers for Disease

Control and Prevention (CDC) requested that doctors inform them of

new flu cases. Yet the picture of the pandemic that emerged was

always a week or two out of date. People might feel sick for days

but wait before consulting a doctor. Relaying the information back

to the central organizations took time, and the CDC only tabulated

the numbers once a week. With a rapidly spreading disease, a

two-week lag is an eternity. This delay completely blinded public

health agencies at the most crucial moments.

   As it happened, a few weeks before the H1N1 virus

made headlines, engineers at the Internet giant Google published a

remarkable paper in the scientific journal Nature. It created a

splash among health officials and computer scientists but was

otherwise overlooked. The authors explained how Google could

“predict” the spread of the winter flu in the United States, not

just nationally, but down to specific regions and even states. The

company could achieve this by looking at what people were searching

for on the Internet. Since Google receives more than three billion

search queries every day and saves them all, it had plenty of data

to work with.

   Google took the 50 million most common search terms

that Americans type and compared the list with CDC data on the

spread of seasonal flu between 2003 and 2008. The idea was to

identify people infected by the flu virus by what they searched for

on the Internet. Others had tried to do this with Internet search

terms, but no one else had as much data, processing power, and

statistical know-how as Google.

   While the Googlers guessed that the searches might

be aimed at getting flu information?—?typing phrases like “medicine

for cough and fever”?—?that wasn’t the point: they didn’t know, and

they designed a system that didn’t care. All their system did was

look for correlations between the frequency of certain search

queries and the spread of the flu over time and space. In total,

they processed a staggering 450 million different mathematical

models in order to test the search terms, comparing its predictions

against actual flu cases from the CDC in 2007 and 2008. And they

struck gold: their software found a combination of 45 search terms

that, when used together in a mathematical model, had a strong

correlation between their prediction and the official figures

nationwide. Like the CDC, they could tell where the flu had spread,

but unlike the CDC they could tell it in near real-time, not a week

or two after the fact.

   Thus when the H1N1 crisis struck in 2009, Google’s

system proved to be a more useful and timely indicator than

government statistics with their natural reporting lags. Public

health officials were armed with valuable information.

   Strikingly, Google’s method does not involve

distributing mouth swabs or contacting physicians’ offices.

Instead, it is built on “big data”?—?the ability of society to

harness information in novel ways to produce useful insights or

goods and services of significant value. With it, by the time the

next pandemic comes around, the world will have a better tool at

its disposal to predict and thus prevent its spread.

Public health is only one area where big data is making a big

difference. Entire business sectors are being reshaped by big data

as well. Buying airplane tickets is a good example.

   In 2003 Oren Etzioni needed to fly from Seattle to

Los Angeles for his younger brother’s wedding. Months before the

big day, he went online and bought a plane ticket, believing that

the earlier you book, the less you pay. On the flight, curiosity

got the better of him and he asked the fellow in the next seat how

much his ticket had cost and when he had bought it. The man turned

out to have paid considerably less than Etzioni, even though he had

purchased the ticket much more recently. Infuriated, Etzioni asked

another passenger and then another. Most had paid less.

   For most of us, the sense of economic betrayal

would have dissipated by the time we closed our tray tables and put

our seats in the full, upright, and locked position. But Etzioni is

one of America’s foremost computer scientists. He sees the world as

a series of big-data problems?—?ones that he can solve. And he has

been mastering them since he graduated from Harvard in 1986 as its

first undergrad to major in computer science.

   From his perch at the University of Washington, he

started a slew of big-data companies before the term “big data”

became known. He helped build one of the Web’s first search

engines, MetaCrawler, which was launched in 1994 and snapped up by

InfoSpace, then a major online property. He co-founded Netbot, the

first major comparison-shopping website, which he sold to Excite.

His startup for extracting meaning from text documents, called

ClearForest, was later acquired by Reuters.

   Back on terra firma, Etzioni was determined to

figure out a way for people to know if a ticket price they see

online is a good deal or not. An airplane seat is a commodity: each

one is basically indistinguishable from others on the same flight.

Yet the prices vary wildly, being based on a myriad of factors that

are mostly known only by the airlines themselves.

   Etzioni concluded that he didn’t need to decrypt

the rhyme or reason for the price differences. Instead, he simply

had to predict whether the price being shown was likely to increase

or decrease in the future. That is possible, if not easy, to do.

All it requires is analyzing all the ticket sales for a given route

and examining the prices paid relative to the number of days before

the departure.

   If the average price of a ticket tended to

decrease, it would make sense to wait and buy the ticket later. If

the average price usually increased, the system would recommend

buying the ticket right away at the price shown. In other words,

what was needed was a souped-up version of the informal survey

Etzioni conducted at 30,000 feet. To be sure, it was yet another

massive computer science problem. But again, it was one he could

solve. So he set to work.

   Using a sample of 12,000 price observations that

was obtained by “scraping” information from a travel website over a

41-day period, Etzioni created a predictive model that handed its

simulated passengers a tidy savings. The model had no understanding

of why, only what. That is, it didn’t know any of the variables

that go into airline pricing decisions, such as number of seats

that remained unsold, seasonality, or whether some sort of magical

Saturday-night-stay might reduce the fare. It based its prediction

on what it did know: probabilities gleaned from the data about

other flights. “To buy or not to buy, that is the question,”

Etzioni mused. Fittingly, he named the research project Hamlet.

   The little project evolved into a venture

capital-backed startup called Farecast. By predicting whether the

price of an airline ticket was likely to go up or down, and by how

much, Farecast empowered consumers to choose when to click the

“buy” button. It armed them with information to which they had

never had access before. Upholding the virtue of transparency

against itself, Farecast even scored the degree of confidence it

had in own predictions and presented that information to users

too.

   To work, the system needed lots of data. To improve

its performance, Etzioni got his hands on one of the industry’s

flight reservation databases. With that information, the system

could make predictions based on every seat on every flight for most

routes in American commercial aviation over the course of a year.

Farecast was now crunching nearly 200 billion flight-price records

to make its predictions. In so doing, it was saving consumers a

bundle.

   With his sandy brown hair, toothy grin, and

cherubic good looks, Etzioni hardly seemed like the sort of person

who would deny the airline industry millions of dollars of

potential revenue. In fact, he set his sights on doing even more

than that. By 2008 he was planning to apply the method to other

goods like hotel rooms, concert tickets, and used cars: anything

with little product differentiation, a high degree of price

variation, and tons of data. But before he could hatch his plans,

Microsoft came knocking on his door, snapped up Farecast for around

$110 million, and integrated it into the Bing search engine. By

2012 the system was making the correct call 75 percent of the time

and saving travelers, on average, $50 per ticket.

   Farecast is the epitome of a big-data company and

an example of where the world is headed. Etzioni couldn’t have

built the company five or ten years earlier. “It would have been

impossible,” he says. The amount of computing power and storage he

needed was too expensive. But although changes in technology have

been a critical factor making it possible, something more important

changed too, something subtle. There was a shift in mindset about

how data could be used.

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编辑推荐

　　本书英文版10%精华内容中文版未作翻译，引用大量案例如Amazon、Google、Twitter、Facebook、苹果及VISA等阐述了“

掌握大数据就掌握了一切”的观点，是关于大数据方面*好的书。

　　阅读作者英文原版，感受一个全新的思考变革、商业变革以及管理变革。

　　"Every decade, there are a handful of books that change the way

you look at everything. This is one of those books. Society has

begun to reckon the change that big data will bring. This book is

an incredibly important start."

　　—Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law

School, and author of Remix and Free Culture

　　"This brilliant book cuts through the mystery and the hype

surrounding big data.

　　A must-read for anyone in business, information technology,

public policy, intelligence, and medicine. And anyone else who is

just plain curious about the future."

　　—John Seely Brown, former Chief Scientist, Xerox Corp., and head

of Xerox Palo Alto Research Center

　　"Big Data breaks new ground in identifying how today’s avalanche

of information fundamentally shifts our basic understanding of the

world. Argued boldly and written beautifully, the book clearly

shows how companies can unlock value, how policymakers need to be

on guard, and how everyone’s cognitive models need to

change."

　　—Joi Ito, Director of the MIT Media Lab

　　"Big Data is a must-read for anyone who wants to stay ahead of

one of the key trends defining the future of business."

　　—Marc Benioff, Chairman and CEO, salesforce.com

　　"An optimistic and practical look at the Big Data revolution —

just the thing to get your head around the big changes already

underway and the bigger changes to come."

　　—Cory Doctorow, boingboing.com

　　"Just as water is wet in a way that individual water molecules

aren’t, big data can reveal information in a way that individual

bits of data can’t. The authors show us the surprising ways that

enormous, complex, and messy collections of data can be used to

predict everything from shopping patterns to flu outbreaks."

　　—Clay Shirky, author of Cognitive Surplus and Here Comes

Everybody

　　"The book teems with great insights on the new ways of harnessing

information, and offers a convincing vision of the future. It is

essential reading for anyone who uses — or is affected by — big

data."

　　—Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity

Analytics

媒体评论

"Every decade, there are a handful of books that change the way

you look at everything. This is one of those books. Society has

begun to reckon the change that big data will bring. This book is

an incredibly important start."

—Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law

School, and author of Remix and Free Culture

"This brilliant book cuts

through the mystery and the hype surrounding big data.

A must-read for anyone in business, information technology,

public policy, intelligence, and medicine. And anyone else who is

just plain curious about the future."

—John Seely Brown, former Chief Scientist, Xerox Corp., and head

of Xerox Palo Alto Research Center

"Big Data breaks new ground in

identifying how today’s avalanche of information fundamentally

shifts our basic understanding of the world. Argued boldly and

written beautifully, the book clearly shows how companies can

unlock value, how policymakers need to be on guard, and how

everyone’s cognitive models need to change."

—Joi Ito, Director of the MIT Media Lab

"Big Data is a must-read for

anyone who wants to stay ahead of one of the key trends defining

the future of business."

—Marc Benioff, Chairman and CEO, salesforce.com

"An optimistic and practical

look at the Big Data revolution — just the thing to get your head

around the big changes already underway and the bigger changes to

come."

—Cory Doctorow, boingboing.com

"Just as water is wet in a way

that individual water molecules aren’t, big data can reveal

information in a way that individual bits of data can’t. The

authors show us the surprising ways that enormous, complex, and

messy collections of data can be used to predict everything from

shopping patterns to flu outbreaks."

—Clay Shirky, author of Cognitive Surplus and Here Comes

Everybody

"The book teems with great

insights on the new ways of harnessing information, and offers a

convincing vision of the future. It is essential reading for anyone

who uses — or is affected by — big data."

—Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity

Analytics

书籍介绍

Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why. We're just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? In this groundbreaking and fascinating book, two of the world's most-respected data experts reveal the reality of a big data world and outline clear and actionable steps that will equip the reader with the tools needed for this next phase of human evolution.

• 作者： Chung-Wan 发布时间：2015-04-08 19:58:45

  太畅销了，所以逆向地选择迟些时候看。。。model只是为人类理解世界画一幅一定比例尺的map，但对N=all，意味着correlation取代causation，并且用correlation而不是causation来做prediction个人仍表示怀疑？

• 作者： 薪火相传 发布时间：2023-06-24 20:53:01

  一如既往的客观，遗憾的是对早期华北地区的战役描写缺失，如张家口失利等，影响了对整个解放战争全局的全面理解。然而瑕不掩瑜，仍是一本难得的好书

  补充一句：这本书的优点是战役描写特别精准、全面，但整个战争的全局画面不如王树增的视角全面

• 作者： 蓝色的鲸 发布时间：2021-12-21 23:43:39

  不够简练不够系统，重复太多，但仍不失为一本好书。作为了解萨提亚流派的入门书籍可以一看。

• 作者： 休云 发布时间：2022-07-12 10:39:22

  一本值得给年轻人读一读的好书~ 真诚 轻松 但是带着深厚的功力。

• 作者： 丹尼尔先生 发布时间：2023-12-28 20:05:58

  目前看来，比较简单

• 作者： 阳明 发布时间：2020-02-06 10:49:44

  1、中国好书和中国最美书获奖，装帧设计精美，把玩享受之书；

  2、顾景舟的壶、文字读来都是享受；

  3、人物传记窥历史点滴得趣；

• 军绅政权、派系斗争和学生运动——读《北洋裂变》

  作者：拿破仑的肩膀 发布时间：2012-09-05 05:35:16

  0. 本书的副标题是“军阀与五四”

        作者想要在书中分析“五四运动”和北洋系分裂的关系。整本书读下来，作者的观点是“五四”其实民间和政府内部对于段祺瑞的皖系政府的不满的总爆发。正如所有的历史问题一样，矛盾都是在不断积累。但是，在北洋系分裂的问题上，这些不断积累的矛盾实在是和五四运动没有半点关系。这书的切入点不错，但很难说这是一本极好的书，不过值得一提的是，这本书的确给人极大的思考空间。

   

  1. 后袁世凯时期北洋政府的分裂局面

        身为北洋系的领头人物，袁世凯毫无疑问是个强权人物。作者在书中提到过，比起孙中山这个手中没有多少兵权的革命家，袁世凯这个手握重兵的军阀才是当时日本彻底殖民中国过程中最为头疼的障碍。所以我们会注意到一个非常奇怪的现象，日本一方面对袁世凯威逼利诱，要求他接受日本提出的灭亡中国的“二十一条”，另一方面，孙中山的革命却得到了大量来自东京的资助和庇护。这种对中国的“政治对冲投资”，恰恰说明了日本政府当时的矛盾心理。

       袁世凯的称帝无疑是对历史进程的逆流而上的徒劳。但是书中提到，面对北洋系以及国内政治军事形势的渐渐的分崩离析，身为强人的袁世凯将称帝视为一个重整局势的机会。我想这种解释很可能是对袁世凯称帝行为的开脱，无论我们纠结于当时的国内形势还是以杨度为首的“筹安会六君子”，其实袁氏自己对极致权利的妄想才是他称帝的最为关键的因素。他刚上大总统不久便修改宪法，将大总统的换届制度改为：继任者是由现任大总统指定三人，名单放于一石盒内，然后再交予议会，这明显就是帝制的演习，可见表面赞同共和的袁氏其内心称帝的想法一直就没有发生根本性的动摇。如果说成为一个优秀的国家领导人需要足够的政治头脑的话，那么成为一个优秀的国家创始人则需要足够的胸怀和气魄了，在这一点上，袁氏显然还差的很远。

       在袁世凯在一片唾骂声中死去之后，继任的实权人物段祺瑞想必也充分意识到了国内对民主共和的迫切需求，可以说当时的北洋政府虽然是个标准的军人政府，却对民主共和的理想有着空前的尊重，这也能解释为什么后来军警对五四时期各个地区一系列的学生运动的镇压显得极其疲软。但继位之初，段祺瑞想要重整河山显然是心有余而力不足，我们不难想象此时资历与段祺瑞不相上下的直系冯国璋，不太可能很给身为皖系领袖的段祺瑞面子。此时虽然北洋系还在维持表面的和平，而各地的大小军阀尤其是非北洋系的西南军阀对于北京政府并不感冒。不过好在日本对中国的态度逐渐趋于缓和，东京以提供巨额的“西原贷款”为契机，迅速缓和了中日的政府间关系。但出于“二十一条”的前车之鉴，民众毫不怀疑表面友善的“西原贷款”内部其实包藏祸心，进而怀疑政府与日本签订了某种秘密卖国条款，这样一来，北洋政府的信任危机进一步加深了。此时段祺瑞重新树立权威的举动是推动了直指广州孙中山革命政权的武力统一战争，而段氏发动这场战争的底气正是来自日本的贷款。不幸的是这场战争以吴佩孚和冯玉祥两位重要的前敌将领发表拒绝作战的“和平通电”而结束。吴佩孚身为一员北洋悍将，公然反对北洋政府的作战命令，这其实就已经是北洋系分裂的开始了。此时吴佩孚是曹锟手下的将领，吴佩孚敢于发“和平通电”，我们有理由猜测他已经得到了身处高层的曹锟的默许。

       这样一来，段祺瑞试图重振北洋系威信的行动以失败告终，北洋系的分裂其实已经不可避免，直到直皖战争爆发，北洋系彻底分崩离析。这里我们可以看到一个基本的政治规律，军队如果完全服从于个人必然会导致分裂和内乱，除非这个个人是国家最高领导人。最典型的例子就是唐代安史之乱之后的各地节度使，握有军权和财权的封疆大吏虽然在一段时间内维持和中央政府的和平关系，但这种中央政府无法控制的地区其实已经变成了独立王国，这样的独立王国的成立就意味着中央政府已经离崩溃不远了。从北洋政府当时的情况分析，这种地方军阀一手遮天的情况，其实从当年清政府准许曾国藩在湖南组织团练湘军以打击太平天国的时候就已经注定了。

   

   

  2. 派系斗争在历史上具有一定的必要性

       派系斗争或者说“党争”其实一直是中国政治的传统。有保守派和改良派的斗争比如宋朝时的王安石变法，有阉党和东学党的斗争比如明朝，北洋政府的派系斗争则更为明显和尖锐最后以致兵戈相见。派系的冲突和斗争必然会导致内耗，但有人的地方就必然有圈子，没有一个政府可以保证自己的政府内部没有圈子和派系。派系斗争是不可避免的，从另一方面来看，派系斗争有时候也是必要的。黄仁宇在《万历十五年》里不断提到“文官集团”的概念。在中国封建政府中，这一庞大的官员团体正是由一个又一个不同大小不同位置的圈子和派系构成的，同乡是一个圈子，科考的“同年”也是一个圈子，还有师生关系、婚姻亲家关系等等，这些不同的圈子和派系有时候会因为追求不同的利益而陷于对立，有时候则因为有共同的利益或共同的敌人而重新组合。正是在这种不断的派系之间和制衡和联合才推动了中国封建社会慢慢的却也不是那么稳定的向前发展。

        我们可以注意到，自宋朝以来，中原政权没有出现过哪怕一个标准意义上的“暴君”，可以想象这和封建统治制度——官僚制度的完善不无关系。很多时候，身为九五之尊的皇帝不得不面对文官集团作为一个整体的压迫，但是，更多的情况是，文官集团这种短暂的团结往往是内部派系斗争的结果。正是这种派系间不断的制衡和对抗，使得政权内部保持小心翼翼的平衡和稳定。这种现象在当代依然可以看到，比如澳门，澳门特首的候选人从来都只有一个人，这正是澳门政界在选举前内部各派别讨论妥协的结果，这样既会照顾到各派系的利益，又不至于走向分裂。再比如，二战之后日本的自民党作为执政党执政长达50余年，除了中间曾经短暂的下野之外，直到本世纪最近一次大选才输给民主党。在自民党执政时期，很多首相的产生正是自民党内部派系斗争和妥协的结果，而这种斗争和妥协并没有拖慢日本在战后迅速腾飞的脚步。

   

   

  3. 学生运动的内在问题

        学生运动一个重大问题就在于没有明确的领导阶级和清晰的斗争纲领。这种没有领导的政治运动最终只有两种结局，一种是走向自身的分裂；另一种是走向极端尤其是走向暴力，然后被政府以合法或非法的手段镇压下去。有意味的事实是，诉求“爱国、民主、进步、科学”的五四运动却以最为暴力的“火烧赵家楼”开始。我迫切的想知道当时那些走上街头的学生是不是一开始就准备以暴力的方式表达自己的情感？抑或是形势所迫不得不开始暴力活动？这种学生运动暴力化而且不可控的趋势正式由于没有明确领导和纲领才导致的。

         我们一年又一年的纪念以爱国主义为内核五四运动，但须知暴力化也是五四运动的显著特征。更为重要的是，爱国主义永远都不能成为暴力活动的免罪金牌。这里我想到的是清末的“义和团运动”，公然进攻别国使馆以及滥杀外国人和教徒的行为实在与野蛮人无异。徐中约在他的《中国近代史》中写道，义和团运动的失败给中国带来的一个显著影响是：宣告之前的民族自强运动的彻底失败，中国的民族自信心遭到了空前的打击，中国人崇洋媚外的心态正是成形于义和团运动失败之后。事实上，自义和团运动以后，中国内部再也没有一次主动对帝国主义入侵的反击，直至第二次世界大战作为同盟国一员获得了胜利，而这种崇洋媚外的心态，到现在也没有完全消除。

        在集体至上的社会中，爱国主义是道德的制高点。中国政治斗争中经典套路就是上纲上线，通俗的解释就是“用对方无法承受的罪名使对方闭嘴”（引自 李亚平《帝国政界往事》），在这种社会中，最为恶毒的上纲上线正是称对方为“卖国贼”。回想最近发生在国内的反日游行，以爱国主义遮羞的砸日本车的暴力活动只能说明内心的脆弱，把日本车车主和日企员工称为卖国贼的行为只是盲目的疯狂。回到五四运动，陆宗舆、曹汝霖和章宗祥这三位亲日官员的确是无可挑剔的出气筒，但我们又不能因为驻日公使签订了某些条约就因此认定他是卖国贼。比如李鸿章签订了《马关条约》，国人都称之为“卖国”。实际上，在马关，李鸿章在谈判期间忍辱负重，甚至还挨了一个日本浪人的打，换了别人去谈，恐怕只能赔的更多。

        继续说1919年的五四，前面提到，由于当时整个中国社会对民主共和的理想以及对学生的尊重，最终导致军警在镇压五四运动的时候表现的扭扭捏捏，甚至出现了学生主动闹事请警察来抓，然后在警察局要求警察道歉的事情。根据这本《北洋裂变》中的引用，当时学生运动的积极分子就有日后中共的早期创始人刘仁静和张国焘。而在上海，五四运动的阶级扩大化的趋势尤其明显，这场自山东发起的学生运动到了上海就已经演变成工人阶级的罢工。然而，上海的警察也大量接到了来自上级的要求保持克制和冷静的命令。

        我们可以想象一下当时那样一个会让社会活动家感觉如沐春风的时代：学生们上街游行，烧了政府大员的住宅。被警察抓起来之后，还有自己的校长和社会名流去保他们出来，有更多的阶级和职业的人们上街，要求释放这些学生。不遵守法制的学生遇到一个不遵守法律的政府，他们都会不约而同的将法制精神置于脑后。段祺瑞的政府最后罢免了那三位亲日派官员，并且释放了学生，但也使北京政府的威信跌到了极点，也为后来汹涌澎湃的学生运动买下了伏笔。

        学生运动的另一个重要的内在问题是，仅仅依靠学生运动很难对政府的产生根本性的影响。原因很简单，政府手中有国家机器，而学生只有热情和装满这热情的胸膛。从北洋政府甚至整个民国政府的历史来看，后袁世凯时期的段祺瑞政府似乎是对共和理想和学生运动最为尊重和克制的时期，直到后来1924年奉系军阀张作霖入主北京，随即开始了对报人及进步人士的大规模逮捕和审判，中共的早起创始人李大钊正是这一时期被害的。从此中国的几乎每一次学生运动都伴随着大规模的逮捕和审判甚至杀害，至于后来“保卫领袖”和“领袖万岁”的口号一出，中国学人的地位从此一落千丈。

       最后想提的一点是，虽然学生运动往往没有明确的领导和组织，但学生运动的确直接的反应了民间的诉求，或者是政府内部某些派系的诉求，甚至可能直接得到了政府内部某些反对派的支持。比如五四运动，当时学生们旗帜鲜明的反对段祺瑞的皖系北洋政府，当时的直系领袖曹锟很可能暗中希望这场运动能充分的打击皖系政府的威信。彼时的舆论几乎一边倒的支持学生，而且不乏大量夸张甚至扭曲的报道，很难说这些报人是不是真的在维持自己独立的报道态度。再比如1976年4月，纪念周恩来的“四五运动”，一场纪念逝世的周恩来的集会变成了呼吁邓小平复出，甚至要求打倒“四人帮”的政治活动。彼时这次运动被定义成“反革命”，但由于国内政治局气氛的迅速变化，“四五运动”很快被平反。事后我们会发现，这些群众运动的诉求不正是那些在“革命”中被打倒的老干部的诉求么？另外，观察1921之后的中国学生运动，无论是“五卅运动”还是“反饥饿反内战反迫害运动”，都能看到中共地下党的组织。由此可见，那时的学生运动已经有了一定的组织和领导，和政治团体的结合也使得学生运动或者群众运动更加有力。

  PS：书写的的确不是特别精彩，但很引人思考。

• 谈笑未必鸿儒，往来也有白丁

  作者：林清明 发布时间：2017-12-01 17:46:42

  蔡澜给倪匡刻过一方印：余有四好，酒色财气。

  是倪匡，也是蔡澜自己。

  香港四大才子，金庸写武侠，倪匡写科幻，黄霑写歌曲，蔡澜提菜篮。

  蔡澜这人很不正经，颇有金庸笔下老顽童的风范。

  蔡澜对吃很有研究，曾任《舌尖上的中国》总顾问，他自己也做过几档美食节目。

  看过《蔡澜谈名菜》，搭配两位美女主持，一路走，一路吃，一路玩。

  那时的蔡澜已近古稀，一头白发，但面色红润，有福相但不算胖，穿黑色中山装，打领带，穿得很正经，但左肩却一直挎着个和尚袋，喝酒吃肉，调戏美女，全无顾忌。

  像南宋高僧，酒肉穿肠过，佛祖心中留。

  中国的文人是要得道升仙的，吞梅嚼雪，吸风饮露，不沾人间半点尘。

  酒是穿肠药，色是惹祸根，财是下山猛虎，气是剐骨钢刀。

  常人避之不及，蔡澜却是笑眯眯地自发迎上前去。

  蔡澜好酒嗜吃，尤好猪油，认为人间美味少了猪油是为不美。 在全世界都鼓吹低脂低糖少吃动物油时，他却说最无聊的一条健康意见就是“不吃猪油”，还曾杜撰了一条养生秘诀： 健康秘诀七个字，抽烟喝酒不运动。

  1983年香港金像奖颁奖典礼上，众人为金像奖最终花落谁家而心惊肉跳，他和倪匡、大岛渚一起，坐在席下，忙里偷闲共分一瓶白兰地。

  蔡澜好色，1989年，蔡澜、倪匡和黄霑一起办了一个节目--《今夜不设防》，邀请的都是香港数一数二的俊男靓女，王祖贤、张国荣、林青霞等等，虽然请的都是大牌，聊天的内容却是各种性爱、情感八卦、吐槽粗口、咸湿段子。而办节目的初衷却只是因为，倪匡爱上了夜总会的一位妈妈桑，经常邀请蔡澜和黄霑到夜总会去玩，三人妙语如珠，逗得夜总会的妈妈桑和小姐们乐不可支，三人一合计，与其花钱娱乐别人，不如把这些东西搬到电视节目上， 请些大明星来，又可以喝酒，又可以看美女，还可以赚钱。 。

  蔡澜爱钱却不贪钱。14岁写稿挖到第一桶金，钱一到手，转头就邀请朋友们上夜总会。金庸请客也能安然受之，理由是，他最有钱。

  蔡澜还曾在寄给亦舒的书信中抱怨：“我们的专栏在星马被报纸转载，从前是没有稿费收的。当然，你和倪匡兄除外。后来我会新加坡，报馆的总编辑请我到他家吃饭。我忘恩负义地吃饭后递律师信给他，结果才在和谈协议下得道零碎的稿酬。吃过甜头后我到马来西亚去，想用同一个办法去对付各报老总。”

  老先生当真好玩。

  蔡澜是个名人，他的好友也都是是个顶个的名人。

  翻翻《江湖老友》目录，金庸、亦舒、黄霑、古龙、黄永玉、冯康侯、丁雄泉、蔡志忠、黄春明、甘建成、张彻、成龙、洪金宝、曾江等等。

  好友之多，名气之大，令人叹服且艳羡。

  更难能可贵的是，蔡澜写友，亦褒亦贬，不缀浮华，不多粉饰，经常笔锋一转，就开始吐槽好友身上的毛病来。

  金庸

  金庸在香港四公子中年龄最长，名气也最大，蔡澜敬重他，称他为查先生，对他的文字赞不绝口，自诩是个“金庸迷”，却总是明里暗里地夸自己是个“一等的金庸迷”，和“镛记”老板甘建成共办“射雕英雄宴”的事写了又写，字里行间都是自得，话里话外都是自夸。

  金庸先生写武侠，古意盎然，有大家之风。私底下却是一个顶好玩的人，只有同样古灵精怪的作家，笔下才能写出那么古灵精怪的黄蓉来。

  金庸作品学术研讨会上，有学者用比较文学的观点来讨论老先生的作品，引述的都是一些偏门的外国作家理论，气氛沉闷，老先生和蔡澜吐槽道：“上次去美国科罗拉多开的学术研讨会也有许多是比较文学的讲者。留个女生出来讲，个个都比较漂亮，所以不闷。”

  老先生身体不好，医生不许吃甜的，查太对查老先生管得很严老先生就和查太斗智斗勇。老先生想了个妙招，把一条长巧克力不知不觉地藏在女护士的围裙袋里面，自己又放了另一条在睡衣口袋里，露出一截。查太没收了他睡衣口袋中的巧克力心满意足地上楼休息，查先生再从护士围裙里扒出巧克力来偷吃。

  明修栈道暗度陈仓，神不知鬼不觉。

  亦舒

  亦舒，人称师太，文风犀利冷辣，有张爱玲之风，金句甚多。

  有很多好的女孩子是不可以娶来做老婆的，有很多好书是不适合睡前阅读的，解决了日常生活问题之后，采可以有心情去买古董，坐靓车，穿皮衣，现在有人送一套水晶杯给你，你有什么用呢？你急需的是一只电饭煲。

  当一个人不再爱他的爱人，她哭闹是错，静默也是错，活着呼吸是错，死了都是错。

  人的天性便是这般凉薄，只要拿更好的来换，一定舍得

  做一个女人要做得像一副画，不要做一件衣裳，被男人试完了又试，却没人买，试残了旧了，五折抛售还有困难。

  我知道金钱只可以买到床而不是睡眠，但躺在床上失眠，总比躺在大街上失眠好。

  亦舒这一章，蔡澜没有另写随笔，放上了近二十多封写给亦舒的信，温馨而平淡，都是一些家常话，可惜没有登上亦舒的回文，甚是失望。

  正面写到亦舒的也只是《方块佳人》中的寥寥几笔。

  亦舒爱骂人，专栏上，男人骂，女人也骂，演员骂，导演也骂。

  有个时代，她棱角尖锐，编辑先生吩咐属下不要得罪她，说，她未够年龄，杀人不偿命。

  可惜不知何故，两人绝交到老死不相往来的地步。

  一声叹息。

  黄霑

  也许有人不知道黄霑，但你一定听过《沧海一声笑》《上海滩》《倩女幽魂》《男儿当自强》。

  他是三口不离黄腔的鬼才，代表了香港最辉煌的电影配乐。

  尽管音乐上的已被封神，但黄霑现实生活中是一个极能闹腾的人。

  学生时代他和李小龙打过架，理所当然被一招撂倒，但他以此为荣，和李小龙过招的机会不是人人都能有的。

  工作之时，他在专栏上连批刘德华三年，大骂：“没有看过，写歌写得那么笨的作词人。”

  填词作曲之余，黄霑有心创作色情小说，刚好遇上周星驰，星爷说：“黄伯，你好好写啊，我妈妈很喜欢你的小说。”向来不以荤段子为耻的黄霑第一次感到尴尬，计划就此搁置。

  黄霑尤喜帅哥靓女，每位上《今夜不设防》的嘉宾，他都要亲亲抱抱。

  晚年黄霑患上癌症，治疗过程不断掉发，黄霑索性全部剃光，邀请麦嘉、罗家英做了一个新节目--《三个光头佬》。

  黄霑活得潇洒，酷似古龙笔下的浪子。

  风流多情，痴爱美人，放荡不羁，挥金买笑。

  蔡澜就在书里爆料了黄霑发生在中学时代的一件风流韵事。

  黄霑中学时迷上了一位很红的舞女，但过夜费很贵，要两千块钱。于是他想了个办法，召集了一百多个同学，大家约好每人出二十，凑齐两千块，然后抽签，抽到谁，就谁代表所有人拿这两千块和舞女春宵一夜。

  同学们都举双手赞成，最后抽签，中奖的当然是黄霑--他是耍老千的好手。

  古龙

  古龙和金庸，同写武侠，文风却大相径庭。一个是“落拓江湖载酒行”的天涯浪子，一个是“侠之大者为国为民”的侠客义士。

  一个像李白，一个像杜甫。

  古龙喜欢喝酒，蔡澜写他喝酒，一杯杯往喉咙中倒进去，是名副其实的倒。不经口腔，直入肠胃。这就是名副其实的酒徒酒鬼了。

  当然，喝酒伤身，这样喝当然容易出事。1985年9月21日，因肝硬化、静脉出血，古龙于台湾去世，终年48岁。

  古龙和林清玄是好友，古龙临终前一个星期，他曾手书一副字赠给来探望他的林清玄： 陌上花发可以缓缓醉矣。

  葬礼上，古龙的生前好友倪匡、蔡澜等人买了48瓶XO陪葬，又担心酒名贵引来盗墓贼，于是开了盖，每人啜饮几口。

  不像葬礼，热闹的像是一场party。

  黄永玉

  黄永玉祖籍湘西凤凰，凤凰还出过一位文人--沈从文，而沈从文是他表叔。

  老先生个子小小，但画画时眉目紧缩，挥毫泼墨间有龙虎之势。

  老先生擅长画彩墨水墨画，融贯中西，不拘礼法，墨块粗犷虬节，色彩斑驳明烈，线条丰富多变，但笔触却是地地道道的中国文人画。

  除了画画，老先生的文字功底也是深厚，他的跋也是一绝。

  世上写历史的永远是两个人：秦始皇写一部，孟姜女写一部。

  郑板桥提倡难得糊涂,其实,真糊涂是天生的,学也学不会.假糊涂却是很费神,还不如别法为好.

  明月几时有。东坡名句，喝醉了酒，哪里还顾得看月亮。

  真有意思。

  老先生在《我是黄永玉》一文中自述：

  余年过七十，称雄板犟，撒恶霸腰，双眼茫茫，早就歇手；喊号吹哨，顶书过河，气力既衰，自觉下台。残年已到，板烟酽茶不断，不咳嗽，不失眠数十年。

  嗜啖多加蒜辣之猪大肠，猪脚，及带板筋之牛肉，洋藿、苦瓜、蕨菜、浏阳豆豉加猪油渣炒青辣子，豆腐干、霉豆豉、水豆豉无一不爱。

  爱喝酒朋友，爱摆龙门阵，爱本地戏，爱好音乐，好书。

  讨厌失礼放肆老少，尤其讨厌油皮涎脸登门求画者，逢此辈必带其到险峻乱木山上乱爬，使其累成孙子，口吐白沫说不成话，直至狼狈逃窜，不见踪影。

  不喝酒，不听卡拉OK，不打麻将及各类纸牌。 不喜欢向屋内及窗外扔垃圾吐痰。此屋亦不让人拍电影及旅游参观。

  此屋即是书中蔡澜提到的“万荷堂”。

  冯康侯

  本人才疏学浅，寡见少闻，对老先生一无所知，只能通过百度百科了解一二。

  曾任黄埔军校校长办公厅秘书，中华书局编辑。书法家，篆刻家。善绘事，凡甲骨铜铭、秦篆汉隶、魏晋碑版无所不精，是为大家。

  蔡澜师从冯康侯学书法和篆刻，两人亦师亦友，情谊浓厚。

  冯老先生教书法，也教做人，一字一句都有禅意。

  老先生学的都是古物，为人却很有新意。

  教学之余，私底下也爱讲俏皮话。

  觉得蔡澜二字字形少，刻印刻不出花样，就想替蔡澜改字。

  老先生一本正经地说：“澜者，大波也，就叫大波好不好？”

  蔡澜发窘，其他师兄弟吃吃笑出声。

  老先生又改：“大波不行，大浪如何？”

  蔡澜更窘，一群师兄弟笑得更开。

  最后，老先生道：“蔡澜就菜篮吧。”

  《江湖老友》除了这些名人，三言两语带过的那些小人物也好玩。

  黄霑去嫖的那名舞女，人长得漂亮，脑子也漂亮，难怪能成为头牌。对自己认得清，对钱也拎得清。

  黄霑凑了两千块来嫖。

  舞女把钱放进皮包后，好奇地问他：“两千块钱不少，你这个小鬼哪来那么多钱？”

  黄霑托盘而出，舞女听了很感动，说：“你那么看得起我，我一定要好好报答你，我要为你做一件我从来没有做过的事，我不收你的钱。”

  黄霑高兴死了，和当红的舞女一夜春宵，不但不用花钱还倒赚了1980块（舞女要是把两千块钱退了，黄霑一定不会还给他的同学，他可能就借花献佛拿这笔钱请客吃饭去了）黄霑觉得自己好运气，竟是遇到了李香君、杜十娘这样的义妓。

  春风一度，第二天舞女真的把钱还给了黄霑。

  舞女退给他二十块钱。

  古龙先生爱喝也爱吃，偶尔也会吃吃咖喱饭，有一家大排档的老板赚了钱就跑舞厅，遇到古龙。古龙去吃的时候，老板故作神秘偷偷一笑，意示彼此守秘，但也不会在饭上多浇一勺咖喱。

  《江湖老友》

  有庙堂之高，也有江湖之远，更有俗世烟火。

  妙哉妙哉。