古诗文大全范文

导语：如何才能写好一篇古诗文大全，这就需要搜集整理更多的资料和文献，欢迎阅读由公务员之家整理的十篇范文，供你借鉴。

篇1

关于古诗文手抄报的图片欣赏

关于古诗文手抄报图片1

关于古诗文手抄报图片2

关于古诗文手抄报图片3

关于古诗文手抄报图片4

关于古诗文手抄报图片5

关于古诗文手抄报的内容：古诗词名言名句

1) 高岸为谷，深谷为陵。——《诗经·小雅》

2) 挽弓当挽强，用箭当用长。射人先射马，擒贼先擒王。——杜甫《前出塞九首》

3) 草木本无意，荣枯自有时。——孟浩然《江上寄山阴崔少府国辅》

4) 李杜文章在，光焰万丈长。——韩愈《调张籍》

5) 采得百花成蜜后，为谁辛苦为谁甜。——罗隐《蜂》

6) 瓜田不纳履，李下不正冠。——汉乐府民歌《君子行》

7) 知人者智，自知者明。——《老子》

8) 一日不见，如三秋兮。——《诗经·王风·采葛》

9) 城中好高髻，四方高一尺。——汉乐府民歌《城中谣》

10) 作诗火急追亡逋，情景一失后难摹。——苏轼《腊日游孤山访惠勒思二僧》

关于古诗文手抄报的资料：谐音古诗

1、《送杜十四之江南》

唐·孟浩然

荆吴相接水为乡(想)，君去春江正渺茫。

日暮征帆何处泊?天涯一望断人肠。

2、《重送裴郎中贬吉州》

唐·刘长卿

猿啼客散暮江头，人自伤心水自流(留)。

同作逐臣君更远，青山万里一孤舟。

推荐其他主题的手抄报资料和图片作为参考：

1.关于古诗的手抄报内容素材

2.关于古诗词配画的手抄报资料素材

3.精美的语文古诗手抄报内容素材

篇2

目录如下：

《三国演义》 《水浒传》 《红楼梦》 《西游记》

《论语》

《朝花夕拾》 《骆驼祥子》

篇3

关键词：劳动安全事故/劳动安全设施/事故隐患/重大伤亡事故/其他严重后果「正 文

    一、如何认定本罪的主体范围

    从《刑法》第135条的规定来看，本罪属于单位犯罪，即本罪的主体只能是工厂、矿山、林场、建筑企业或者其他企业、事业单位及其中对重大劳动安全事故负有直接责任的人员。

    至于本罪中单位的范围，与重大责任事故罪中的单位完全一样，即包括工厂、矿山、林场、建筑企业等企业、事业单位，以及群众合伙经营组织和个体经营户。不管这些单位是否公有制单位，是否依法成立，也不管这些单位是否以从事生产、作业活动为主业，即便某些企业、事业单位不以从事生产、作业活动为主业，但只要其中有从事生产、作业活动的部门也包括在内。[1]

    单位中对重大劳动安全事故负有直接责任的人员，既包括单位中的直接管理、维护劳动安全设施的人员，也包括单位中负责主管劳动安全设施的人员。至于这些人员是不是单位的正式职工，是一直从事劳动安全设施管理、维护工作的职工还是临时被安排从事该工作的职工，对成为本罪的主体没有影响。这里还有两个问题值得研究：第一，上述两类人员在不知道劳动安全设施不符合国家规定从而存在发生人员伤亡事故的隐患，同时也不知道有关部门或者本单位职工已经提出了本单位劳动安全设施不符合国家规定及存在发生人员伤亡事故隐患的情况时，是否承担本罪的刑事责任？根据《刑法》第135条的规定，要让该两类人员承担本罪的刑事责任，必须是劳动安全设施不符合国家规定存在事故隐患并且有关部门或者本单位职工已经向他们提出该情况后，仍然不采取措施排除事故隐患，因而发生重大伤亡事故的情形。那么，不管该两类人员事实上是否知道劳动安全设施不符合国家规定而存在发生事故的隐患，只要其不知道这种情况已经被有关部门或者本单位职工提出的，就不应要求他们承担本罪的刑事责任。当然，也可能存在这样一些比较少见的情况，即有关部门或者本单位职工要向该两类人员提出本单位的劳动安全设施不符合国家规定而存在发生事故隐患的情况时，该两类人员本来应当在工作岗位上值班，但是由于某种非正当的理由而不在，而使事故隐患没能被该两类人员采取措施予以排除，并发生了重大伤亡的事故。客观而言，这种情况下该两类人员对重大伤亡事故的发生是负有不可推卸的责任的。但是，从《刑法》第135条的规定来看，却无法对该两类人员追究本罪的刑事责任。这当然是刑法规定的不周全之处，有待于今后改进。

    第二，有关主管单位劳动安全设施管理、维护工作的负责人在已经向直接负责管理、维护本单位劳动安全设施的人员如何采取有力措施排除事故隐患作了安排后，后者并没有执行或者没有按照要求执行，由此发生重大伤亡事故的，应否承担本罪的刑事责任？根据前者担负的职责，其不仅负有安排后者对劳动安全设施进行具体管理、维护的职责，而且还负有对后者的工作进行监督、检查的职责。在其对后者的工作情况没有检查或者虽然进行了检查但明知后者没有按照自己的要求进行工作而不管不顾的，他仍然对重大伤亡事故的发生有不可推卸的刑事责任。当然，由于其并不是从事劳动安全设施管理、维护具体工作的人员，因此，他对事故的发生仅负有次要的责任。如果他不仅安排后者采取有力措施排除事故隐患，又进行了检查，且认为后者采取的措施已经足以排除事故隐患，即便客观上后者采取的措施并不足以排除事故隐患，在发生重大伤亡事故时，也不宜要求他承担刑事责任。

    二、如何理解本罪的主观方面

篇4

“从根本上说，我们认为，无论什么经济组织，实现多样化都是取得成功的重要秘诀之一，一家公司的董事会，一家风险投资公司，一个管理团队，无不如此。”方斯坦说，“虽然多样性并不仅仅体现在性别上，但性别往往能够成为试金石。”高表示：“要打破‘男性主导’这种商业定势很难，最迅速的方式就是女性自己创办公司，并以此入手来打造男女平等的商业文化。”

方斯坦期望，女性看问题的视角能够成为实现良好投资回报的主要动力之一。她表示：“不同的视角能让人开扩视野，发现并把握住更多的机会。我们崇尚合作，不会试图独占市场，也不会排挤天使投资人。”

但方斯坦和高也都强调，她们并不想把“女性做创投”作为吸引人的看点，只是想像男性同行一样，在职业生涯中谱写自己的篇章。她们致力于早期阶段投资，让种子投资、天使投资有机会与大机构实现对接。同时，她们还希望通过聚焦早期阶段投资，避免做晚期投资时经常会遭遇的估值泡沫。创办Aspect Partners之前，方斯坦和高分别供职于德丰杰和Accel Partners，她们一共主导了26笔投资，项目总市值达100亿美元。

Aspect Partners的主要投资方向是移动通讯领域的初创企业，关注移动通讯对社会发展造成的冲击性影响，关注大数据、数据安全和健康领域。该公司最近领投了同样由女性领导的求职网站Muse，该轮投资规模达1000万美元。虽然方斯坦和高都表示，公司不仅限于投资由女企业家领导的企业，但截至目前，她们投资的项目中有40%都属于这类项目，与之形成对照的是，美国市场上由女性领导或参与创办的企业不到企业总数的20%。方斯坦表示，热衷使用移动通信服务和App的女性比男性多20%～25%，这也激发了女性投身移动创业领域的热情，因为Aspect Partners关注移动领域投资，所以投资的女性主导项目也显得多一些。在这方面，该公司比较有代表性的投资项目包括女性创办的美妆电商Birchbox、时尚珠宝电商BaubleBar，以及由女性CEO管理的保姆服务公司Urban Sitter。由男性担任CEO的企业中，Aspect Partners投资了网络安全公司ForeScout、移动医疗保健平台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.