狐偃

Multi tool usebody.skin-minerva .mw-parser-output table.infobox captiontext-align:center
狐偃 |
---|
姓 | 姬
|
---|
氏 | 狐
|
---|
名 | 偃 |
---|
字 | 子犯 |
---|
别名 | 咎犯、舅犯 |
---|
时代 | 春秋 |
---|
国家 | 晉國 |
---|
身份 | 卿 |
---|
官位 | 晉國上軍將 |
---|
父 | 狐突
|
---|
子女 | 狐射姑
|
---|
狐偃,姬姓,狐氏,字子犯。是晋文公的舅舅,又稱舅犯、咎犯、臼犯。
生平
狐偃出身戎狄部落。其父狐突,在晋武公時出仕晉國。其兄狐毛。武公之子晋献公娶狐突的女儿生重耳和夷吾,狐偃和兄长狐毛輔助重耳。
驪姬之乱使晋国混乱,狐偃劝重耳流亡外国。重耳开始了19年国外流亡。开始住在北狄,后来继位为晋惠公的夷吾密谋行刺重耳,重耳与属下大夫们流亡中原。
途中、重耳一行经过卫国五鹿,向种地的农民乞求食物,农民将土块放于容器中給重耳[1]。重耳(據中華書局本《史記》載,是趙衰而非狐偃勸阻重耳:「趙衰曰『土者,有土也,君其拜受之。』」但北大本《春秋左傳正義》載,是子犯勸阻重耳。子犯乃狐偃字。)大怒,要鞭打农民,狐偃说农民献土,说明公子得到土地啊,重耳大喜。前636年,在赵衰、狐偃、胥臣、先轸的辅佐下重耳成为国君晋文公,狐偃帮助晋文公成为霸主,狐偃为上军将,是晋文公的首席谋士,之后,其子狐射姑继承了他的爵位。遗物有“子犯和钟”,现藏台北国立故宫博物院。[2]
参考文献
- 《史记》
- 《左传》
- 《韩非子·外储说·右上》:‘何如足以战民?’[3]
^ 杨伯峻《春秋左传注》:“‘乞食于野人,野人与之块’块,土块也。晋世家作‘野人盛土器中进之’,盖器者,公子乞食所用者也”
^ 子犯和鐘1-12、舊連結、舊連結存檔 - 臺灣國立故宮博物院
^ 晋文公问于狐偃曰:“寡人甘肥周于堂,卮酒豆肉集于宫,壶酒不清,生肉不布,杀一牛遍于国中,一岁之功尽以衣士卒,其足以战民乎?”狐子曰:“不足。”文公曰:“吾弛关市之征而缓刑罚,其足以战民乎?”狐子对曰:“不足。”文公曰:“吾民之有丧资者,寡人亲使郎中视事,有罪者赦之,贫穷不足者与之,其足以战民乎?”狐子对曰:“不足。此皆所以慎产也;而战之者,杀之也。民之从公也,为慎产也,公因而迎杀之,失所以为从公矣。”曰:然则何如足以战民乎?”狐子对曰:“令无得不战。”公曰:“无得不战奈何?”狐子对曰:“信赏必罚,其足以战。”公曰:“刑罚之极安至?”对曰:“不辟亲贵,法行所爱。”文公曰:“善。”明日,令田于圃陆,期以日中为期,后期者行军法焉。于是公有所爱者曰颠颉后期,吏请其罪,文公陨涕而忧。吏曰:“请用事焉。”遂斩颠颉之脊,以徇百姓,以明法之信也。而后百姓皆惧曰:“君于颠颉之贵重如彼甚也,而君犹行法焉,况于我则何有矣。”文公见民之可战也,于是遂兴兵伐原,克之。伐卫,东其亩,取五鹿。攻阳。胜虢。伐曹。南围郑,反之陴;罢宋围。还与荆人战城濮,大败荆人;返为践土之盟,遂成衡雍之义:一举而八有功。所以然者,无他故异物,从狐偃之谋,假颠颉之脊也。
Bs7,lFHB6uu hV0JsI6N,4Vct4GO,3KyT,ayel11AYkEx Vq AGtFsZi GgXvPxk,sFHS9p
Popular posts from this blog
Ramiro Burr's New Blog - to go back: www.ramiroburr.com From Latin rock to reggaeton, boleros to blues,Tex-Mex to Tejano, conjunto to corridos and beyond, Ramiro Burr has it covered. If you have a new CD release, a trivia question or are looking for tour info, post a message here or e-mail Ramiro directly at: musicreporter@gmail.com Top Tejano songwriter Luis Silva dead of heart attack at 64 By Ramiro Burr on October 23, 2008 8:40 AM | Permalink | Comments (12) | TrackBacks (0) UPDATE: Luis Silva Funeral Service details released Visitation 4-9 p.m. Saturday, Rosary service 6 p.m. Saturday at Porter Loring, 1101 McCullough Ave Funeral Service 10:30 a.m. Monday St. Anthony De Padua Catholic Church, Burial Service at Chapel Hills, 7735 Gibbs Sprawl Road. Porter Loring (210) 227-8221 Related New Flash: Irma Laura Lopez: long time record promoter killed in accident NewsFlash: 9:02 a.m. (New comments below) Luis Silva , one of the most well-known ...
1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...