ジョン・ヴェネマン
Multi tool useジョン・ヴェネマン（John G. Veneman, 1925年10月31日 - 1982年4月11日）は、アメリカ合衆国の政治家。共和党所属。
生涯
1925年にカリフォルニア州コーコランで誕生[1]。1962年にカリフォルニア州下院議員に選出され、以降1968年まで5期連続で務めた[1]。1969年に州下院議員を辞職[1]。1969年から1973年まで保健教育福祉次官[2][3][4]。1974年にカリフォルニア州副知事に立候補するも敗北[1]。1982年にカリフォルニア州サクラメントで死去[1]。
栄誉
カリフォルニア州道99号線のスタニスラウス郡から州道132号線に至る区間は、彼にちなんで「ジョン・G・ヴェネマン・フリーウェイ」と命名された[5][# 1]。娘は第27代アメリカ合衆国農務長官アン・ヴェネマン[1]。
備考
出典
- ^ abcdefAlex Vassar & Shane Meyers. “John G. Veneman”. 2011年10月29日閲覧。
^ A common thread of Service, p. 44. Lists holders of the position of Under Secretary. "John G. Veneman March 6, 1969 to present". The book was published in 1970
^ Derthick, Martha, "Policymaking for social security" (1979), p. 68. "The Nixon administration of 1969-72 continued the practice of liberal appointees with Robert H. Finch (1969-1970) and Richardson (1970-1973) as secretaries and John G. Veneman (1969-1973) as under secretary".
^ Kaplowitz, Craig Allan, "LULAC, Mexican Americans and National Policy" (2005), p. 147. "As John Veneman, undersecretary of HEW, told The Washington Post in January, 1972, ""Whenever Spanish-speaking students' performance is shown to be markedly lower, a strong case can be made that they are not receiving an equal education."" Teaching children in a language that some understand and others do not was not ""equal"", according to Veneman, and Spanish language use and low test scores together could prove the need for remedy."
^ Daniel P. Faigin. “California Highways”. 2011年10月29日閲覧。
公職
|
|---|
先代：
ジェイムズ・ヘンリー・マクロックリン
|
アメリカ合衆国保健教育福祉次官
1969年3月 - 1973年1月
| 次代：
フランク・カールッチ
|
LPMGW9okr4T1YlxTgP
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...
