導(dǎo)語(yǔ)：如何才能寫(xiě)好一篇古詩(shī)文大全，這就需要搜集整理更多的資料和文獻(xiàn)，歡迎閱讀由公務(wù)員之家整理的十篇范文，供你借鑒。

篇1

關(guān)于古詩(shī)文手抄報(bào)的圖片欣賞

關(guān)于古詩(shī)文手抄報(bào)圖片1

關(guān)于古詩(shī)文手抄報(bào)圖片2

關(guān)于古詩(shī)文手抄報(bào)圖片3

關(guān)于古詩(shī)文手抄報(bào)圖片4

關(guān)于古詩(shī)文手抄報(bào)圖片5

關(guān)于古詩(shī)文手抄報(bào)的內(nèi)容：古詩(shī)詞名言名句

1) 高岸為谷，深谷為陵。——《詩(shī)經(jīng)·小雅》

2) 挽弓當(dāng)挽強(qiáng)，用箭當(dāng)用長(zhǎng)。射人先射馬，擒賊先擒王。——杜甫《前出塞九首》

3) 草木本無(wú)意，榮枯自有時(shí)。——孟浩然《江上寄山陰崔少府國(guó)輔》

4) 李杜文章在，光焰萬(wàn)丈長(zhǎng)。——韓愈《調(diào)張籍》

5) 采得百花成蜜后，為誰(shuí)辛苦為誰(shuí)甜。——羅隱《蜂》

6) 瓜田不納履，李下不正冠。——漢樂(lè)府民歌《君子行》

7) 知人者智，自知者明。——《老子》

8) 一日不見(jiàn)，如三秋兮。——《詩(shī)經(jīng)·王風(fēng)·采葛》

9) 城中好高髻，四方高一尺。——漢樂(lè)府民歌《城中謠》

10) 作詩(shī)火急追亡逋，情景一失后難摹。——蘇軾《臘日游孤山訪惠勒思二僧》

關(guān)于古詩(shī)文手抄報(bào)的資料：諧音古詩(shī)

1、《送杜十四之江南》

唐·孟浩然

荊吳相接水為鄉(xiāng)(想)，君去春江正渺茫。

日暮征帆何處泊?天涯一望斷人腸。

2、《重送裴郎中貶吉州》

唐·劉長(zhǎng)卿

猿啼客散暮江頭，人自傷心水自流(留)。

同作逐臣君更遠(yuǎn)，青山萬(wàn)里一孤舟。

推薦其他主題的手抄報(bào)資料和圖片作為參考：

1.關(guān)于古詩(shī)的手抄報(bào)內(nèi)容素材

2.關(guān)于古詩(shī)詞配畫(huà)的手抄報(bào)資料素材

3.精美的語(yǔ)文古詩(shī)手抄報(bào)內(nèi)容素材

篇2

目錄如下：

《三國(guó)演義》 《水滸傳》 《紅樓夢(mèng)》 《西游記》

《論語(yǔ)》

《朝花夕拾》 《駱駝祥子》

篇3

關(guān)鍵詞：勞動(dòng)安全事故/勞動(dòng)安全設(shè)施/事故隱患/重大傷亡事故/其他嚴(yán)重后果「正 文

    一、如何認(rèn)定本罪的主體范圍

    從《刑法》第135條的規(guī)定來(lái)看，本罪屬于單位犯罪，即本罪的主體只能是工廠、礦山、林場(chǎng)、建筑企業(yè)或者其他企業(yè)、事業(yè)單位及其中對(duì)重大勞動(dòng)安全事故負(fù)有直接責(zé)任的人員。

    至于本罪中單位的范圍，與重大責(zé)任事故罪中的單位完全一樣，即包括工廠、礦山、林場(chǎng)、建筑企業(yè)等企業(yè)、事業(yè)單位，以及群眾合伙經(jīng)營(yíng)組織和個(gè)體經(jīng)營(yíng)戶。不管這些單位是否公有制單位，是否依法成立，也不管這些單位是否以從事生產(chǎn)、作業(yè)活動(dòng)為主業(yè)，即便某些企業(yè)、事業(yè)單位不以從事生產(chǎn)、作業(yè)活動(dòng)為主業(yè)，但只要其中有從事生產(chǎn)、作業(yè)活動(dòng)的部門(mén)也包括在內(nèi)。[1]

    單位中對(duì)重大勞動(dòng)安全事故負(fù)有直接責(zé)任的人員，既包括單位中的直接管理、維護(hù)勞動(dòng)安全設(shè)施的人員，也包括單位中負(fù)責(zé)主管勞動(dòng)安全設(shè)施的人員。至于這些人員是不是單位的正式職工，是一直從事勞動(dòng)安全設(shè)施管理、維護(hù)工作的職工還是臨時(shí)被安排從事該工作的職工，對(duì)成為本罪的主體沒(méi)有影響。這里還有兩個(gè)問(wèn)題值得研究：第一，上述兩類人員在不知道勞動(dòng)安全設(shè)施不符合國(guó)家規(guī)定從而存在發(fā)生人員傷亡事故的隱患，同時(shí)也不知道有關(guān)部門(mén)或者本單位職工已經(jīng)提出了本單位勞動(dòng)安全設(shè)施不符合國(guó)家規(guī)定及存在發(fā)生人員傷亡事故隱患的情況時(shí)，是否承擔(dān)本罪的刑事責(zé)任？根據(jù)《刑法》第135條的規(guī)定，要讓該兩類人員承擔(dān)本罪的刑事責(zé)任，必須是勞動(dòng)安全設(shè)施不符合國(guó)家規(guī)定存在事故隱患并且有關(guān)部門(mén)或者本單位職工已經(jīng)向他們提出該情況后，仍然不采取措施排除事故隱患，因而發(fā)生重大傷亡事故的情形。那么，不管該兩類人員事實(shí)上是否知道勞動(dòng)安全設(shè)施不符合國(guó)家規(guī)定而存在發(fā)生事故的隱患，只要其不知道這種情況已經(jīng)被有關(guān)部門(mén)或者本單位職工提出的，就不應(yīng)要求他們承擔(dān)本罪的刑事責(zé)任。當(dāng)然，也可能存在這樣一些比較少見(jiàn)的情況，即有關(guān)部門(mén)或者本單位職工要向該兩類人員提出本單位的勞動(dòng)安全設(shè)施不符合國(guó)家規(guī)定而存在發(fā)生事故隱患的情況時(shí)，該兩類人員本來(lái)應(yīng)當(dāng)在工作崗位上值班，但是由于某種非正當(dāng)?shù)睦碛啥辉?，而使事故隱患沒(méi)能被該兩類人員采取措施予以排除，并發(fā)生了重大傷亡的事故?？陀^而言，這種情況下該兩類人員對(duì)重大傷亡事故的發(fā)生是負(fù)有不可推卸的責(zé)任的。但是，從《刑法》第135條的規(guī)定來(lái)看，卻無(wú)法對(duì)該兩類人員追究本罪的刑事責(zé)任。這當(dāng)然是刑法規(guī)定的不周全之處，有待于今后改進(jìn)。

    第二，有關(guān)主管單位勞動(dòng)安全設(shè)施管理、維護(hù)工作的負(fù)責(zé)人在已經(jīng)向直接負(fù)責(zé)管理、維護(hù)本單位勞動(dòng)安全設(shè)施的人員如何采取有力措施排除事故隱患作了安排后，后者并沒(méi)有執(zhí)行或者沒(méi)有按照要求執(zhí)行，由此發(fā)生重大傷亡事故的，應(yīng)否承擔(dān)本罪的刑事責(zé)任？根據(jù)前者擔(dān)負(fù)的職責(zé)，其不僅負(fù)有安排后者對(duì)勞動(dòng)安全設(shè)施進(jìn)行具體管理、維護(hù)的職責(zé)，而且還負(fù)有對(duì)后者的工作進(jìn)行監(jiān)督、檢查的職責(zé)。在其對(duì)后者的工作情況沒(méi)有檢查或者雖然進(jìn)行了檢查但明知后者沒(méi)有按照自己的要求進(jìn)行工作而不管不顧的，他仍然對(duì)重大傷亡事故的發(fā)生有不可推卸的刑事責(zé)任。當(dāng)然，由于其并不是從事勞動(dòng)安全設(shè)施管理、維護(hù)具體工作的人員，因此，他對(duì)事故的發(fā)生僅負(fù)有次要的責(zé)任。如果他不僅安排后者采取有力措施排除事故隱患，又進(jìn)行了檢查，且認(rèn)為后者采取的措施已經(jīng)足以排除事故隱患，即便客觀上后者采取的措施并不足以排除事故隱患，在發(fā)生重大傷亡事故時(shí)，也不宜要求他承擔(dān)刑事責(zé)任。

    二、如何理解本罪的主觀方面

篇4

“從根本上說(shuō)，我們認(rèn)為，無(wú)論什么經(jīng)濟(jì)組織，實(shí)現(xiàn)多樣化都是取得成功的重要秘訣之一，一家公司的董事會(huì)，一家風(fēng)險(xiǎn)投資公司，一個(gè)管理團(tuán)隊(duì)，無(wú)不如此?！狈剿固拐f(shuō)，“雖然多樣性并不僅僅體現(xiàn)在性別上，但性別往往能夠成為試金石?！备弑硎荆骸耙蚱啤行灾鲗?dǎo)’這種商業(yè)定勢(shì)很難，最迅速的方式就是女性自己創(chuàng)辦公司，并以此入手來(lái)打造男女平等的商業(yè)文化。”

方斯坦期望，女性看問(wèn)題的視角能夠成為實(shí)現(xiàn)良好投資回報(bào)的主要?jiǎng)恿χ?。她表示：“不同的視角能讓人開(kāi)擴(kuò)視野，發(fā)現(xiàn)并把握住更多的機(jī)會(huì)。我們崇尚合作，不會(huì)試圖獨(dú)占市場(chǎng)，也不會(huì)排擠天使投資人?！?/p>

但方斯坦和高也都強(qiáng)調(diào)，她們并不想把“女性做創(chuàng)投”作為吸引人的看點(diǎn)，只是想像男性同行一樣，在職業(yè)生涯中譜寫(xiě)自己的篇章。她們致力于早期階段投資，讓種子投資、天使投資有機(jī)會(huì)與大機(jī)構(gòu)實(shí)現(xiàn)對(duì)接。同時(shí)，她們還希望通過(guò)聚焦早期階段投資，避免做晚期投資時(shí)經(jīng)常會(huì)遭遇的估值泡沫。創(chuàng)辦Aspect Partners之前，方斯坦和高分別供職于德豐杰和Accel Partners，她們一共主導(dǎo)了26筆投資，項(xiàng)目總市值達(dá)100億美元。

Aspect Partners的主要投資方向是移動(dòng)通訊領(lǐng)域的初創(chuàng)企業(yè)，關(guān)注移動(dòng)通訊對(duì)社會(huì)發(fā)展造成的沖擊性影響，關(guān)注大數(shù)據(jù)、數(shù)據(jù)安全和健康領(lǐng)域。該公司最近領(lǐng)投了同樣由女性領(lǐng)導(dǎo)的求職網(wǎng)站Muse，該輪投資規(guī)模達(dá)1000萬(wàn)美元。雖然方斯坦和高都表示，公司不僅限于投資由女企業(yè)家領(lǐng)導(dǎo)的企業(yè)，但截至目前，她們投資的項(xiàng)目中有40%都屬于這類項(xiàng)目，與之形成對(duì)照的是，美國(guó)市場(chǎng)上由女性領(lǐng)導(dǎo)或參與創(chuàng)辦的企業(yè)不到企業(yè)總數(shù)的20%。方斯坦表示，熱衷使用移動(dòng)通信服務(wù)和App的女性比男性多20%～25%，這也激發(fā)了女性投身移動(dòng)創(chuàng)業(yè)領(lǐng)域的熱情，因?yàn)锳spect Partners關(guān)注移動(dòng)領(lǐng)域投資，所以投資的女性主導(dǎo)項(xiàng)目也顯得多一些。在這方面，該公司比較有代表性的投資項(xiàng)目包括女性創(chuàng)辦的美妝電商Birchbox、時(shí)尚珠寶電商BaubleBar，以及由女性CEO管理的保姆服務(wù)公司Urban Sitter。由男性擔(dān)任CEO的企業(yè)中，Aspect Partners投資了網(wǎng)絡(luò)安全公司ForeScout、移動(dòng)醫(yī)療保健平臺(tái)Vida Health等。

篇5

Key wordsItem response theory； Mixed-type models； Dichotomous items； Polytomous items； Maximum likelihood estimation； Weighted likelihood estimation

CLC numberO 211.2Document codeA

1Introduction

So far， there are a lot of approaches about the bias of the ability estimation reduction have been proposed. For instance， Warm （1998） proposed a WML for application in tests of dichotomous items. The WML estimator consistently displayed the smaller level of bias than the MLE estimator. Then Penfield and Bergeron （2005） generalized the WML to the polytomous IRT models （Samejima， 1998； Wang， 2001； Penfield and Bergeron， 2005）. Compared to dichotomous items， polytomous items provide superior information concerning the level of the estimated latent trait（Donoghue， 1994； Embretson and Reise， 2000， p.95； Jodoin， 2003； Penfield and Bergeron， 2005）.

These approaches mentioned above focused separately on tests composed of either dichotomous items or polytomous items. However， in practice the mixed-type test composing of both dichotomous and polytomous items is more commonly used， for instance the National Assessment of Educational Progress （NAEP）. Therefore， we believe that it is of interest to propose a WML that applied to the mixed-type test. This is the main work of this article.

The purpose of this article is twofold： （a） to present the derivations of the WML estimator under a mixed-type item response model and （b） to compare the properties of the WML estimator to that of the ML estimators under different test conditions. To this end， the remainder of this article is organized into four sections. First， two models used in the article are briefly summarized and present the derivations of the WML under the mixed-type test. Second， a simulation study is conducted to evaluate the performance of the proposed WML by comparing with the usual MLE. Third， a real data set from a large-scale reading assessment is used to demonstrate the difference between the two estimation procedures. Finally， we conclude the article with discussion.

2.1RASCH and PCM

In this paper， we consider a mixed-type model that is the combination of the following Rasch model （RM） and the partial-credit model （PCM）. To simplify the notation， the examinee subscript will not be shown in the following derivations.

The RM is defined as

where Pij（θ） is the probability of selecting response j of polytomous item i at ability levelθ， bivdenotes the step parameter of item i of category v， and m denotes the number of the response category.

2.2The Weighted Maximum Likelihood Estimator

To facilitate the presentation of the estimation method， the relevant technical aspects of the IRT ability estimation methods are described below. Based on the above RM model and PCM， the likelihood of response can be written as the product of two types of likelihood functions：

3.1The Design of This Simulation

To evaluate the performance of the proposed WML method， an intensive simulation study was conducted to cover a wide range of index values， such as the total number of items and the proportion of dichotomous and polytomous items in a mixed-type test.

Except for the WML method， the MLE method is also used to estimated the latent traitθ. Then， we compare the two estimators under nine different test conditions， they are： three short test （6 items with 4， 3， and 2 dichotomously scored items）， three medium test （12 items with 8， 6， and 4 dichotomously scored items） and three long test （24 items with 16， 12， and 8 dichotomously scored items）. The difficulty parameters of the dichotomous items were randomly generated from the standard normal distribution N（0，1）.The location parameters of each polytomous item were randomly generated from four normal distributions： bi1～N（0，0.2），bi2～N（？2，0.2），bi3～N（0.5，0.2），bi4～N（2，0.2）. Furthermore， 17 equally spacedθvalues were considered， ranging from -4.0 to +4.0 within an increment 0.5. For each of 17 levels ofθ， 1000 vectors of responses to the n items were generated. The dichotomous item responses were simulated according to the RASCH model， and the polytomous item responses were simulated according to the PCM. For each vector of responses， the WML and ML estimators were computed. For the trials containing response patterns consisting of all zeros or all out， the Newton-Raphson algorithm cannot converge， and thus the ML and WML estimators could not be obtained. These response patterns were removed from the analysis， and examinee responses were simulated until admissible response patterns were obtained at each of the 17 levels ofθ.

3.3Results of Simulation

Impact of Inadmissible Response Patterns. Before describing the results concerning the relative performance of the two estimators， the reader should be aware of an anomaly in the results that was immediately apparent upon inspection of the simulation output. For values of less than -2.0 and greater than 2.0， the values of bias in the ML and WML estimators became grossly and nonsensically inflated in magnitude， such that the values of the ML and WML estimators were pulled in toward zero. Note that this direction in bias is opposite of what is typically observed for the ML estimator （Warm， 1989）. This obscure result can be explained by the removal of all response patterns consisting of all zeros or all out. As a result of the nonsensical results obtained for the conditions in which |θ| > 2.0， the following description of the simulation results focuses on the comparison of the properties of the WML and ML estimators only for the conditions in which |θ|≤2.0.

standard deviation of the sampling distribution of a statistic， we also compare of the SD and the SE for the WML. The simulation results show that WML outperforms MLE regarding reduction in Bias， RMSE and SD.

4Real Data Analysis

level of the 2000 examinees base on WML and the MLE procedures are？0.6835 and？0.6123.

The total absolute difference and the total relative difference of estimated abilities based on the two procedures are respectively，

The primary limitation to this study is thefinding of nonsensical results for the ML and WML estimators for extreme levels of the latent trait （|θ| > 2.0）. As described in the“results of simulation”section， nonsensical ML and WML estimates for this situation are attributable to the removal of trials for which the response pattern consisted of all zeros or all out and poses a major hurdle to the valid use of the ML and WML estimators for such extreme levels ofθ. As a result， extreme caution should be used when estimatingθusing tests and scales that do not match the respondent’s level of latent trait.

There are some other issues that should be further explored. First， the proposed weighting scheme can be generalized to a broad range of applications. For examples， it can be applied to computerized adaptive testing （CAT）， not only to lower item exposure rates， but also to improve ability estimation （e.g.， Tao 2011）. Second， although the RM and the PCM are used commonly in practice test， there are some other more general item response models， for instance the three-parameter logistic（3PL） model and the General Partial Credit Model （GPCM）. Therefore， it is worth studying to extend the WML to these more complex models. Third， in addition to the ML， the Bayesian estimator， for instance the expected a posteriori （EAP） estimator， is frequently used in IRT， so a procedure of reduce the bias of the Bayesian estimator should be discussed.

Appendix

Details of Weighted Maximum Likelihood Estimation Using the Newton-Raphson Algorithm The weighted likelihood estimator is the solution of Equation （10） as follows：

References

[1] Baker F B. Item response theory： Parameter estimation techniques. New York： Marcel Dekker， 1992.

[2] Donoghue J R. An empirical examination of the IRT information of polytomously scored reading items under the generalized partial credit model. Journal of Educational Measurement， 1994， 41， 295-311.

[3] Embretson S E， Reise S P. Item response theory for psychologists. Mahwah， NJ： Erlbaum， 2000.

[4] Jodoin M G. Measurement efficiency of innovative item formats in computerbased testing. Journal of Educational Measurement， 2003， 40， 15.

[5] Penfield R D， and Bergeron J M. Applying a weighted maximum likelihood latent trait estimator to the generalized partial credit model. Applied Psychological Measurement， 2005， 29， 218-233.