The model used as the basis for regulation of human radiation exposures in the United States has been a source of controversy for decades because human health consequences have not been determined with statistically meaningful certainty for the dose levels allowed for radiation workers and the general public. This dissertation evaluates the evolution of the science and regulation of radiation health effects in the United States since the early 1900s using actor-network theory and the concept of co-production of science and social order. This approach elucidated the ordering instruments that operated at the nexus of the social and the natural in making institutions, identities, discourses, and representations, and the sociotechnical imaginaries animating the use of those instruments, that culminated in a regulatory system centered on the linear no-threshold dose-response model and the As Low As Reasonably Achievable philosophy. The science of radiation health effects evolved in parallel with the development of radiation-related technologies and the associated regulatory system. History shows the principle of using the least amount of radiation exposure needed to achieve the desired effect became established as a social convention to help avoid inadvertent harm long before there was a linear no-threshold dose-response model. Because of the practical need to accept some level of occupational radiation exposure, exposures from medical applications of radiation, and some de minimis exposure to the general public, the ALARA principle emerged as an important ordering instrument even before the linear no-threshold model had gained wide support. Even before ALARA became the law, it had taken hold in a manner that allowed the nuclear industry to rationalize its operations as representing acceptable levels of risk, even though it could not be proven that the established exposure limits truly precluded harm to the exposed individuals. Laboratory experiments and epidemiology indicated that a linear dose-response model appeared suitable as a "cautious assumption" by the 1950s. The linear no-threshold model proved useful to both the nuclear establishment and its detractors. In the hands of proponents of nuclear technologies, the model predicted that occupational exposures and exposures to the public represented small risks compared to naturally occurring levels of radiation and other risks that society deemed acceptable. Conversely, opponents of nuclear technologies used the model to advance their causes by predicting health impacts for undesirable numbers of people if large populations received small radiation exposures from sources such as fallout from nuclear weapon testing or effluents from nuclear reactor operations. In terms of sociotechnical imaginaries, the linear no-threshold model was compatible with both of the dominant imaginaries involved in the actor-network. In the technocratic imaginary of institutions such as the Atomic Energy Commission, the model served as a tool for qualified experts to make risk-informed decisions about applications of nuclear technologies. In the socially progressive imaginary of the citizen activist groups, the model empowered citizens to formulate arguments informed by science and rooted in the precautionary principle to challenge decisions and actions by the technocratic institutions. This enduring dynamic tension has led to the model retaining the status of "unproven but useful" even as the underlying science has remained contested. / Doctor of Philosophy / This dissertation provides a social science perspective on an enduring paradox of the nuclear industry: why is regulation of radiation exposure based on a model that everyone involved agrees is wrong? To answer that question, it was necessary to delve into the history of radiation science to establish how safety regulation began and evolved along with the understanding of radiation's health effects. History shows the philosophy of keeping radiation exposures as small as possible for any given application developed long ago when the health effects of radiation were very uncertain. This practice turned out to be essential as science started to indicate that there may not be a safe threshold dose below which radiation exposure had no potential for health consequences. By the 1950s, a combination of theory, experiments, health studies of the survivors of the World War II atomic bombings, and other evidence suggested that the risk of cancer was proportional to the amount of radiation a person received (i.e., linear). Although this "linear no-threshold" model was far from proven, both sides used it in debates over nuclear weapon testing and safety standards for nuclear reactors in the 1950s through the early 1970s. Since the model predicted small health risks for the levels of radiation experienced by radiation workers and the public, nuclear advocates used it to argue that the risks were smaller than many other risks that people accept every day. At the same time, opposing activists used the model to argue that small cancer likelihoods added up to a lot of cancers when large populations were exposed. This decades-long discourse effectively institutionalized the model. The model's "unproven but useful" status was strengthened in the early 1970s when the Atomic Energy Commission supplemented its numeric exposure limits by turning the longtime practice of dose minimization into a requirement. This "As Low As Reasonably Achievable" requirement plays a vital role in rationalizing why a non-zero exposure limit is safe enough despite the fact that the linear no-threshold model treats any amount of radiation as harmful.

內生經濟成長認為政府支出結構與收入結構均會影響恆定狀態成長率之觀點和事實是否一致呢？許多先前相關之研究需被修正，因為這些研究在政府預算限制式的設定並不完全，導致實證之結果產生偏誤。本文除了將討論忽略這些偏誤所會導致之影響外，進一步將以One-Way與TwO-Way的固定效果與隨機效果模型來進行估計，並修正先前研究所發生之問題，然後找出強力的證據來支持Barro模型（1990）所做的預測。本文的樣本資料取自東亞經濟前瞻與亞洲開發銀行，以亞洲新、舊四小龍，加上申國，共九個國家的17年時間序列資料所形成的揉合資料（pooling data）為實證對象。分析結果發現在考慮異質性與同期相關性下，若採用似無相關迴歸技巧來對One-Way固定效果模型進行估計，則：（1）不論政府是以租稅收入或是非租稅收入作為融通財源，資本性支出的增加均會提高經濟成長率，經常性支出的增加則均會對經濟成長率造成負面影響；（2）在政府以租稅收入為融通來源下，非租稅收入增加將會降低經濟成長率；若政府以非租稅收入作為融通之財源，則此時租稅收入之增加將會降低經濟成長。 〔關鍵字〕資本支出、經常支出、同期相關性（Contemporaneous correlation）、異質性（HetereSkedaStiCity）、One-Way fixed（random）effect model、Two-way fixed（random）effect model、似無相關迴歸（Seemingly unrelated regression）

自1978年中國開始經濟的改革開放政策以來，吸引外資便成為其推動經濟成長的重要手段之一。1992年鄧小平南巡，再一次宣示建立「社會主義的市場經濟體制」後，由於開放的經濟政策更為明朗，使越來越多的國家到中國直接投資。自此，中國成為全世界最受歡迎的外資投資國之一。甚至在2002年超越美國，成為全世界最大的外資接受國。如此多的國家對中國展開大規模的投資，其投資的規模與投資地區的選擇卻有相當大的差異。因此，本文的研究目的，將以1993至2003年世界各國對中國投資的追蹤資料（panel data），搭配固定效果模型（fixed-effect model）與隨機效果模型（random-effect model）的估計，並以目前中國沿海發展具代表性的北京、上海與廣東三個省（市）之比較，探究影響世界各國對中國直接投資的決定因素。 實證結果發現影響外商直接投資北京、上海與廣東的決定因素中，顯著影響的變數為相對工資率、對中國貿易依存度、相對匯率以及相對借貸成本。而其餘的變數，如相對國內生產毛額、相對每人國內生產毛額和相對國家風險等皆不顯著，反映了其皆非外商直接投資中國時所考量的決定因素。 / Since 1978, China has adopted the so-called “open door policy”, attracting foreign direct investment (FDI) has become one of the most important methods to facilitate its economic growth. However, foreign countries didn’t invest large amount toward China until Deng Xiaoping’s southern trip in 1992. The purpose of this study is thus to investigate the determinants of FDI from different foreign countries to different regions in China after foreign countries started to invest tremendous amount toward China. This study adopts fixed-effect model and random-effect model to investigate the determinants of FDI in China with panel data of Beijing, Shanghai and Guangdong during the period of 1993-2003. The result shows relative wage rate, trade dependence to China, relative exchange rate and relative borrowing cost are the most important factors in attracting FDI in Beijing, Shanghai and Guangdong during 1993-2003. Finally, in order to reduce the mistakes occurred in positive models and enable the study more rigorous, then uses more methods to test the models and the result.

This paper adopts a quantitative and qualitative lens, through which we explore the economic relationship between the region of Sub-Saharan Africa and China as a consequence of Chinese Foreign direct investments (FDI).  Primarily, the paper’s direction, analysis, and discussions are dictated by the usage of primary and secondary data. Our primary data is constructed as a panel data set including GDP for 38 countries alongside the Chinese FDIs for each country during the period 2003-2021. Our secondary data includes already existing papers on topics related to our purpose and the data found in them. The main points include the economic development of Sub-Saharan Africa, the subsequent enforced Chinese economic policy, and further the gains China see from their strides into the region. In order to prove our hypothesis and research questions we've used econometric models.The panel data was used to conduct several time series regressions, where we further used different models and tests to get the best possible result Due to the fact that our regression shows a significant and positive correlation between economic growth in the region as a result of Chinese FDI, we can establish that past research is reinforced. Through the used model we’re enabled to look at the individual effects of every country in the region. In addition, by comparing these effects with the level of corruption and which type of income group the countries belong to, we can further highlight the fact that China tends to invest in countries who can offer something in return. Examples of such cases could be South Africa and Angola who are two of the biggest receivers of Chinese FDI in the region, which we, throughout the paper, attribute to the fact that they have an abundance of natural resources.

近年來中國籍與外籍配偶家庭快速的增加，成為台灣社會備受到關注的一個新興議題。面對日益普遍的台灣外籍配偶現象，本文以2001年至2005年台灣各縣市的追蹤資料，並以固定效果模型分別針對整體、中國籍以及東南亞籍外籍女性配偶進行估計，來檢驗影響台灣外籍女性配偶規模的決定因素。總體而言，本文發現台灣外籍配偶現象已相當普遍，在經濟高度發展下，選擇外籍配偶並不只侷限在發展相對落後的地區。 此外，薪資所得高低會影響外籍配偶的規模，即表示多數的外籍配偶家庭經濟情況屬於中低階層，與一般社會大眾認知相同。而失業率與整體外籍配偶規模的估計結果呈現負相關，亦即失業率越高的地區，外籍配偶規模越小。顯示在失業率居高不下的情形下，降低台灣男性結婚的動機，以及外籍配偶來台意願。但是東南亞籍的外籍配偶在失業率高的地方規模較大，與本文實證變數假設相符。 最後，由模型的固定效果與時間效果分析可知，各縣市間並無顯著的特質效果影響外籍配偶的規模，也就是說外籍配偶的現象在台灣已經相當普遍。而由時間效果來看，外籍配偶的熱潮近兩年來有顯著趨緩的現象，這是否意味著外籍配偶所帶來之社會問題層出不窮，帶給民眾過多的負面印象，使得未婚男性對於選擇外籍配偶有所保留，值得後續研究來檢驗。 / In recent years, foreign bride has become a new issue, and there are more and more families with foreign brides in Taiwan. This study adopts fixed-effect model to investigate the determinants of foreign bride’s scale in Taiwan with 23 regions’ panel data during the period of 2001-2005. The result shows the phenomenon of foreign bride is general in Taiwan society. In addition, wage and unemployment rate are the most important factors to affect foreign bride’s scale in Taiwan. Wage reflects the living standard, and most families with foreign brides in Taiwan are bottom feeders. Furthermore, unemployment rage has a negative relationship to foreign bride’s scale in Taiwan, and the amount of foreign bride has a decreasing trend. Finally, in order to reduce the mistakes occurred in positive models and enable the study more rigorous, then uses more methods to the models and the result.

<p>In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. </p><p>Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. </p><p>The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.</p>

A “Value-Added” definition of school effectiveness calls for the evaluation of schools based on the unique contribution of schools to individual student academic growth. The estimates of value-added school effectiveness are usually used for ranking and classifying schools. The current simulation study examined and compared the validity of school effectiveness estimates in four statistical models for school ranking and classification. The simulation study was conducted under two sample size conditions and the situations typical in school effectiveness research. The Conditional Cross-Classified Model (CCCM) was used to simulate data. The findings indicated that the gain score model adjusting for students’ test scores at the end of kindergarten (i. e., prior entering to an elementary school) (Gain_kindergarten) could validly rank and classify schools. Other models, including the gain score model adjusting for students’ test scores at the end of Grade 4 (i. e., one year before estimating the school effectiveness in Grade 5) (Gain_grade4), the Unconditional Cross-Classified Model (UCCM), and the Layered Mixed Effect Model (LMEM), could not validly rank or classify schools. The failure of the UCCM model in school ranking and classification indicated that ignoring covariates would distort school rankings and classifications if no other analytical remedies were applied. The failure of the LMEM model in school ranking and classification indicated that estimation of correlations among repeated measures could not alleviate the damage caused by the omitted covariates. The failure of the Gain_grade4 model cautioned against adjustment using the test scores of the previous year. The success of the Gain_kindergarten model indicated that under some circumstances, it was possible to achieve valid school rankings and classifications with only two time points of data.

This paper examines the impact of Information and Communication Technology (ICT) on health using the data of 61 countries between 2000 and 2009 from the World Bank. The ICT variables considered in this paper include internet, fixed phones, and mobile phones. Based on the Millennium Development Goals (MDGs) of the United Nations, we select several health variables and examine the impact of ICT on these variables. These variables include life expectancy at birth, infant mortality rate, under-five mortality rate, maternal mortality ratio, and prevalence of HIV. The estimation strategies are the pooling OLS model, the fixed effect model, and the random effect model. The empirical results suggest that ICT indeed plays a significant role in improving the health level of a country. ICT effectively decreases infant mortality rates and children mortality rates, and also increases life expectancy. This finding supports the viewpoints of United Nations (UN), World Health Organization (WHO), World Bank, and International Telecommunication Union (ITU) that ICT has great potential in improving a country¡¦s health. The finding also confirms the arguments of several literatures, including McNamara (2007) and Lucas (2008), that ICT can lead to a more effective health system. In addition, we also find that fixed phones and mobile phones, which have more powerful functions in communicating and have greater flexibility, help decrease deaths due to acute diseases or emergencies; while internet displays more profound impact on improving health with the accumulation of time. Our results suggest that adopting and promoting ICT is an effective way for developing countries and less-developed countries to enhance the level of health of people. We also expect that ICT can help these countries to meet at least part of the Millennium Development Goals.

A “Value-Added” definition of school effectiveness calls for the evaluation of schools based on the unique contribution of schools to individual student academic growth. The estimates of value-added school effectiveness are usually used for ranking and classifying schools. The current simulation study examined and compared the validity of school effectiveness estimates in four statistical models for school ranking and classification. The simulation study was conducted under two sample size conditions and the situations typical in school effectiveness research. The Conditional Cross-Classified Model (CCCM) was used to simulate data. The findings indicated that the gain score model adjusting for students’ test scores at the end of kindergarten (i. e., prior entering to an elementary school) (Gain_kindergarten) could validly rank and classify schools. Other models, including the gain score model adjusting for students’ test scores at the end of Grade 4 (i. e., one year before estimating the school effectiveness in Grade 5) (Gain_grade4), the Unconditional Cross-Classified Model (UCCM), and the Layered Mixed Effect Model (LMEM), could not validly rank or classify schools. The failure of the UCCM model in school ranking and classification indicated that ignoring covariates would distort school rankings and classifications if no other analytical remedies were applied. The failure of the LMEM model in school ranking and classification indicated that estimation of correlations among repeated measures could not alleviate the damage caused by the omitted covariates. The failure of the Gain_grade4 model cautioned against adjustment using the test scores of the previous year. The success of the Gain_kindergarten model indicated that under some circumstances, it was possible to achieve valid school rankings and classifications with only two time points of data.

This thesis explores how economic performance, measured as real GDP-growth per capita, affects the vote share of parties which relies on radical ideological platforms. Using a fixed effect model with panel data, based on real electoral outcomes of 18 western European democracies, the result reveals an ambiguous reality. The overall conclusion implies that low growth rates benefit the electoral success of radical-right parties and holds for robustness checks. No solid evidence of the relationship is found on the radical-left side.The result further reveals that the individuals decision to vote radical is relative more affected by the ongoing business cycle trend between the elections rather than sudden changes close to the election day. The relationship also appears to be stronger among nations of southern Europe.