1,720,984 research outputs found
Replication Data for: How Cross-Validation Can Go Wrong and What to Do About it.
No full textThe introduction of new “machine learning” methods and terminology to political science complicates the interpretation of results. Even more so, when one term – like cross-validation – can mean very different things. We find different meanings of cross-validation in applied political science work. In the context of predictive modeling, cross-validation can be used to obtain an estimate of true error or as a procedure for model tuning. Using a single cross-validation procedure to obtain an estimate of the true error and for model tuning at the same time leads to serious misreporting of performance measures. We demonstrate the severe consequences of this problem with a series of experiments. We also observe this problematic usage of cross-validation in applied research. We look at Muchlinski et al. (2016) on the prediction of civil war onsets to illustrate how the problematic cross-validation can affect applied work. Applying cross-validation correctly, we are unable to reproduce their findings. We encourage researchers in predictive modeling to be especially mindful when applying cross-validation
Replication Data for: How Cross-Validation Can Go Wrong and What to Do About it.
No full textThe introduction of new “machine learning” methods and terminology to political science complicates the interpretation of results. Even more so, when one term – like cross-validation – can mean very different things. We find different meanings of cross-validation in applied political science work. In the context of predictive modeling, cross-validation can be used to obtain an estimate of true error or as a procedure for model tuning. Using a single cross-validation procedure to obtain an estimate of the true error and for model tuning at the same time leads to serious misreporting of performance measures. We demonstrate the severe consequences of this problem with a series of experiments. We also observe this problematic usage of cross-validation in applied research. We look at Muchlinski et al. (2016) on the prediction of civil war onsets to illustrate how the problematic cross-validation can affect applied work. Applying cross-validation correctly, we are unable to reproduce their findings. We encourage researchers in predictive modeling to be especially mindful when applying cross-validation
Replication Data for: How to Improve the Substantive Interpretation of Regression Results when the Dependent Variable is Logged
No full textRegression models with log-transformed dependent variables are widely used by social scientists to investigate nonlinear relationships between variables. Unfortunately, this transformation complicates the substantive interpretation of estimation results and often leads to incomplete and sometimes even misleading interpretations. We focus on one valuable but underused method, the presentation of quantities of interest such as expected values or first differences on the original scale of the dependent variable. The procedure to derive these quantities differs in seemingly minor but critical aspects from the well-known procedure based on standard linear models. To improve empirical practice, we explain the underlying problem and develop guidelines that help researchers to derive meaningful interpretations from regression results of models with log-transformed dependent variables
Replication Data for: The Role of Hyperparameters in Machine Learning Models and How to Tune Them
No full textHyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model’s performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31%) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models
Replication Data for: The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election
No full textWe present the Zweitstimme Model to forecast the outcome of the 2021 German federal election. The dynamic Bayesian forecasting model combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and generates probability statements about other quantities of interest, such as the majority for coalitions in parliament, and the expected overall size of parliament. As a central expectation, 100 days before the election, our model predicts a strengthening in Green party support (17% [12%; 23%]) and new majorities for coalition governments (67% chance for a majority of a CDU/CSU-Green coalition). The model further expects the Bundestag to grow in size to around 814 seats
Replication Data for: Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals
No full textWe offer a dynamic Bayesian forecasting model for multi-party elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multi-party nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multi-party setting
Replication Data for: Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals
No full textWe offer a dynamic Bayesian forecasting model for multi-party elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multi-party nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multi-party setting
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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