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Repairs : the added value of being wrong
Grammatical structures connect systems of thought and articulation, the conditions of which hardly seem to fit each other. Repairs are productive mechanisms that solve translation problems between modules or levels by adapting derivations or representations to requirements that have to be met unconditionally. Compensating for derivational and interpretive defects, repairs determine core properties of natural language grammars and their interfaces.
Book
An evidence-based evaluation of transferrable skills and job satisfaction for science PhDs
PhD recipients acquire discipline-specific knowledge and a range of relevant skills during their training in the life sciences, physical sciences, computational sciences, social sciences, and engineering. Empirically testing the applicability of these skills to various careers held by graduates will help assess the value of current training models. This report details results of an Internet survey of science PhDs (n = 8099) who provided ratings for fifteen transferrable skills. Indeed, analyses indicated that doctoral training develops these transferrable skills, crucial to success in a wide range of careers including research-intensive (RI) and non-research-intensive (NRI) careers. Notably, the vast majority of skills were transferrable across both RI and NRI careers, with the exception of three skills that favored RI careers (creativity/innovative thinking, career planning and awareness skills, and ability to work with people outside the organization) and three skills that favored NRI careers (time management, ability to learn quickly, ability to manage a project). High overall rankings suggested that graduate training imparted transferrable skills broadly. Nonetheless, we identified gaps between career skills needed and skills developed in PhD training that suggest potential areas for improvement in graduate training. Therefore, we suggest that a two-pronged approach is crucial to maximizing existing career opportunities for PhDs and developing a career-conscious training model: 1) encouraging trainees to recognize their existing individual skill sets, and 2) increasing resources and programmatic interventions at the institutional level to address skill gaps. Lastly, comparison of job satisfaction ratings between PhD-trained employees in both career categories indicated that those in NRI career paths were just as satisfied in their work as their RI counterparts. We conclude that PhD training prepares graduates for a broad range of satisfying careers, potentially more than trainees and program leaders currently appreciate.
Journal Article
What Do Transnational Terrorists Target? Has It Changed? Are We Safer?
This article utilizes Bayesian Poisson changepoint regression models to demonstrate how transnational terrorists adjusted their target choices in response to target hardening. In addition, changes in the collective tastes of terrorists and their sponsorship have played a role in target selection over time. For each of four target types—officials, military, business, and private parties—the authors identify the number of regimes and the probable predictors of the events. Regime changes are tied to the rise of modern transnational terrorism, the deployment of technological barriers, the start of state sponsorship, and the dominance of the fundamentalists. The authors also include two sets of covariates—logistical outcome and victim's nature—to better explain the dynamics. As other targets have been fortified and terrorists have sought greater carnage, private parties have become the preferred target type. In recent years, terrorists have increasingly favored people over property for all target types. Moreover, authorities have been more successful at stopping attacks against officials and the military, thereby motivating terrorists to attack business targets and private parties.
Journal Article
ConflLlama: Domain-specific adaptation of large language models for conflict event classification
We present ConflLlama, demonstrating how efficient fine-tuning of large language models can advance automated classification tasks in political science research. While classification of political events has traditionally relied on manual coding or rigid rule-based systems, modern language models offer the potential for more nuanced, context-aware analysis. However, deploying these models requires overcoming significant technical and resource barriers. We demonstrate how to adapt open-source language models to specialized political science tasks, using conflict event classification as our proof of concept. Through quantization and efficient fine-tuning techniques, we show state-of-the-art performance while minimizing computational requirements. Our approach achieves a macro-averaged AUC of 0.791 and a weighted F1-score of 0.753, representing a 37.6% improvement over the base model, with accuracy gains of up to 1463% in challenging classifications. We offer a roadmap for political scientists to adapt these methods to their own research domains, democratizing access to advanced NLP capabilities across the discipline. This work bridges the gap between cutting-edge AI developments and practical political science research needs, enabling broader adoption of these powerful analytical tools.
Journal Article
Terrorist attack and target diversity: Changepoints and their drivers
Terrorists choose from a wide variety of targets and attack methods. Unlike past literature, this article investigates how diversity in target choice and attack modes among domestic and transnational terrorists has evolved and changed over the past 40 years. Changes in the practice of homeland security, which affects the marginal costs of target—attack combinations, and changes in the dominant terrorist influence at the global level, which affects the marginal benefits of target—attack combinations, drive the changepoints. Our empirical analysis relies on count data drawn from the Global Terrorism Database (GTD) for 1970—2010 that distinguishes between domestic and transnational terrorist incidents. Given the data-intensity requirements of our methods, the study is necessarily from a global perspective. A Bayesian Reversible Jump Markov chain Monte Carlo (RJMCMC) changepoint analysis is applied to identify arrival rate changes in both domestic and transnational terrorism. The changepoints in these aggregate series are then matched with those of the subset time series for attack modes (e.g. assassinations and bombings) and target types (e.g. officials and private parties). The underlying drivers of these changepoints are then identified. The article also calculates a Herfindahl index of attack diversity for the aggregate and component domestic and transnational terrorism time series for the entire period and during four subperiods. The variation in both domestic and transnational terrorist attacks has generally fallen over the last four decades; nevertheless, this diversity still remains high. Bombings of private parties have become the preferred target—attack combination for both transnational and domestic terrorists. This combination is the hardest-to-defend target—attack combination and requires the most homeland security resources. Policymakers can use these and other results to focus their counter-terrorism measures.
Journal Article
Development and assessment of a sustainable PhD internship program supporting diverse biomedical career outcomes
A doctoral-level internship program was developed at the University of North Carolina at Chapel Hill with the intent to create customizable experiential learning opportunities for biomedical trainees to support career exploration, preparation, and transition into their postgraduate professional roles. We report the outcomes of this program over a 5-year period. During that 5-year period, 123 internships took place at over 70 partner sites, representing at least 20 academic, for-profit, and non-profit career paths in the life sciences. A major goal of the program was to enhance trainees’ skill development and expertise in careers of interest. The benefits of the internship program for interns, host/employer, and supervisor/principal investigator were assessed using a mixed-methods approach, including surveys with closed- and open-ended responses as well as focus group interviews. Balancing stakeholder interests is key to creating a sustainable program with widespread support; hence, the level of support from internship hosts and faculty members were the key metrics analyzed throughout. We hypothesized that once a successful internship program was implemented, faculty culture might shift to be more accepting of internships; indeed, the data quantifying faculty attitudes support this. Furthermore, host motivation and performance expectations of interns were compared with results achieved, and this data revealed both expected and surprising benefits to hosts. Data suggests a myriad of benefits for each stakeholder group, and themes are cataloged and discussed. Program outcomes, evaluation data, policies, resources, and best practices developed through the implementation of this program are shared to provide resources that facilitate the creation of similar internship programs at other institutions. Program development was initially spurred by National Institutes of Health pilot funding, thereafter, successfully transitioning from a grant-supported model, to an institutionally supported funding model to achieve long-term programmatic sustainability.
Journal Article
Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides
Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.
Journal Article
Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict
We propose a framework for forecasting and analyzing regional and international conflicts. It generates forecasts that (1) are accurate but account for uncertainty, (2) are produced in (near) real time, (3) capture actors' simultaneous behaviors, (4) incorporate prior beliefs, and (5) generate policy contingent forecasts. We combine the CAMEO event-coding framework with Markov-switching and Bayesian vector autoregression models to meet these goals. Our example produces a series of forecasts for material conflict between the Israelis and Palestinians for 2010. Our forecast is that the level of material conflict between these belligerents will increase in 2010, compared to 2009.
Journal Article
True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries
Background
Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy.
Methods
Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion.
Results
The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic.
Conclusions
In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.
Journal Article
Faculty perceptions and knowledge of career development of trainees in biomedical science: What do we (think we) know?
The Broadening Experiences in Scientific Training (BEST) program is an NIH-funded effort testing the impact of career development interventions (e.g. internships, workshops, classes) on biomedical trainees (graduate students and postdoctoral fellows). BEST Programs seek to increase trainees' knowledge, skills and confidence to explore and pursue expanded career options, as well as to increase training in new skills that enable multiple career pathways. Faculty mentors are vital to a trainee's professional development, but data about how faculty members of biomedical trainees view the value of, and the time spent on, career development are lacking. Seven BEST institutions investigated this issue by conducting faculty surveys during their BEST experiment. The survey intent was to understand faculty perceptions around professional and career development for their trainees. Two different, complementary surveys were employed, one designed by Michigan State University (MSU) and the other by Vanderbilt University. Faculty (592) across five institutions responded to the MSU survey; 225 faculty members from two institutions responded to the Vanderbilt University survey. Participating faculty were largely tenure track and male; approximately 1/3 had spent time in a professional position outside of academia. Respondents felt a sense of urgency in introducing broad career activities for trainees given a recognized shortage of tenure track positions. They reported believing career development needs are different between a graduate student and postdoctoral fellow, and they indicated that they actively mentor trainees in career development. However, faculty were uncertain as to whether they actually have the knowledge or training to do so effectively. Faculty perceived that trainees themselves lack a knowledge base of skills that are of interest to non-academic employers. Thus, there is a need for exposure and training in such skills. Faculty stated unequivocally that institutional support for career development is important and needed. BEST Programs were considered beneficial to trainees, but the awareness of local BEST Programs and the national BEST Consortium was low at the time surveys were employed at some institutions. It is our hope that the work presented here will increase the awareness of the BEST national effort and the need for further career development for biomedical trainees.
Journal Article