Do you want to publish a course? Click here

Election forensic analysis of the Turkish Constitutional Referendum 2017

93   0   0.0 ( 0 )
 Added by Peter Klimek
 Publication date 2017
and research's language is English




Ask ChatGPT about the research

With a majority of Yes votes in the Constitutional Referendum of 2017, Turkey continues its transition from democracy to autocracy. By the will of the Turkish people, this referendum transferred practically all executive power to president Erdogan. However, the referendum was confronted with a substantial number of allegations of electoral misconducts and irregularities, ranging from state coercion of No supporters to the controversial validity of unstamped ballots. In this note we report the results of an election forensic analysis of the 2017 referendum to clarify to what extent these voting irregularities were present and if they were able to influence the outcome of the referendum. We specifically apply novel statistical forensics tests to further identify the specific nature of electoral malpractices. In particular, we test whether the data contains fingerprints for ballot-stuffing (submission of multiple ballots per person during the vote) and voter rigging (coercion and intimidation of voters). Additionally, we perform tests to identify numerical anomalies in the election results. We find systematic and highly significant support for the presence of both, ballot-stuffing and voter rigging. In 6% of stations we find signs for ballot-stuffing with an error (probability of ballot-stuffing not happening) of 0.15% (3 sigma event). The influence of these vote distortions were large enough to tip the overall balance from No to a majority of Yes votes.



rate research

Read More

Over the past decade, the field of forensic science has received recommendations from the National Research Council of the U.S. National Academy of Sciences, the U.S. National Institute of Standards and Technology, and the U.S. Presidents Council of Advisors on Science and Technology to study the validity and reliability of forensic analyses. More specifically, these committees recommend estimation of the rates of occurrence of erroneous conclusions drawn from forensic analyses. Black box studies for the various subjective feature-based comparison methods are intended for this purpose. In general, black box studies often have unbalanced designs, comparisons that are not independent, and missing data. These aspects pose difficulty in the analysis of the results and are often ignored. Instead, interpretation of the data relies on methods that assume independence between observations and a balanced experiment. Furthermore, all of these projects are interpreted within the frequentist framework and result in point estimates associated with confidence intervals that are confusing to communicate and understand. We propose to use an existing likelihood-free Bayesian inference method, called Approximate Bayesian Computation (ABC), that is capable of handling unbalanced designs, dependencies among the observations, and missing data. ABC allows for studying the parameters of interest without recourse to incoherent and misleading measures of uncertainty such as confidence intervals. By taking into account information from all decision categories for a given examiner and information from the population of examiners, our method also allows for quantifying the risk of error for the given examiner, even when no error has been recorded for that examiner. We illustrate our proposed method by reanalysing the results of the Noblis Black Box study by Ulery et al. in 2011.
In the past few decades, constitution-making processes have shifted from closed elite writing to incorporating democratic mechanisms. Yet, little is known about democratic participation in deliberative constitution-making processes. Here, we study a deliberative constituent process held by the Chilean government between 2015 and 2016. The Chilean process had the highest level of citizen participation in the world ($204,402$ people, i.e., $1.3%$ of the population) for such a process and covered $98%$ of the national territory. In its participatory phase, people gathered in self-convoked groups of 10 to 30 members, and they collectively selected, deliberated, and wrote down an argument on why the new constitution should include those social rights. To understand the citizen participation drivers in this volunteer process, we first identify the determinants at the municipality level. We find the educational level, engagement in politics, support for the (left-wing) government, and Internet access increased participation. In contrast, population density and the share of evangelical Christians decreased participation. Moreover, we do not find evidence of political manipulation on citizen participation. In light of those determinants, we analyze the collective selection of social rights, and the content produced during the deliberative phase. The findings suggest that the knowledge embedded in cities, proxied using education levels and main economic activity, facilitates deliberation about themes, concepts, and ideas. These results can inform the organization of new deliberative processes that involve voluntary citizen participation, from citizen consultations to constitution-making processes.
Risk-limiting post-election audits limit the chance of certifying an electoral outcome if the outcome is not what a full hand count would show. Building on previous work, we report on pilot risk-limiting audits in four elections during 2008 in three California counties: one during the February 2008 Primary Election in Marin County and three during the November 2008 General Elections in Marin, Santa Cruz and Yolo Counties. We explain what makes an audit risk-limiting and how existing and proposed laws fall short. We discuss the differences among our four pilot audits. We identify challenges to practical, efficient risk-limiting audits and conclude that current approaches are too complex to be used routinely on a large scale. One important logistical bottleneck is the difficulty of exporting data from commercial election management systems in a format amenable to audit calculations. Finally, we propose a bare-bones risk-limiting audit that is less efficient than these pilot audits, but avoids many practical problems.
When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoes sole. If its accidentals correspond to those of a suspects shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability---the chance that a shoe chosen at random would match the crime scene prints accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the Presidents Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.
Internet of Things is revolutionizing the current era with its vast usage in number of fields such as medicine, automation, home security, smart cities, etc. As these IoT devices uses are increasing, the threat to its security and to its application protocols are also increasing. Traffic passing over these protocol if intercepted, could reveal sensitive information and result in taking control of the entire IoT network. Scope of this paper is limited to MQTT protocol. MQTT (MQ Telemetry Transport) is a light weight protocol used for communication between IoT devices. There are multiple brokers as well as clients available for publishing and subscribing to services. For security purpose, it is essential to secure the traffic, broker and end client application. This paper demonstrates extraction of sensitive data from the devices which are running broker and client application.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا