This paper describes a coding algorithm and corresponding dataset of negotiation interventions and negotiation interactions by country parties and groupings in the multilateral negotiations under the United Nations Framework Convention on Climate Change (UNFCCC). The data is obtained by scraping and automatically coding the negotiation summaries published in the Earth Negotiations Bulletins (ENBs) between 1995 and 2023. The data is validated by comparing it with a hand-coded dataset of negotiation interactions under the UNFCCC. One limitation discovered upon validation is that our automated procedure finds significantly fewer opposition interactions than the hand-coding procedure. The main reason for this is that the algorithm identifies negotiation interactions on the basis of individual sentences, while the hand coding is able to identify them across sentences and even paragraphs. However, the distribution of opposition interactions seems to be representative of the larger dataset and therefore not substantively biased. We describe possible uses of this data in research, and provide the algorithm, which can be adapted for application to other negotiations covered by the ENBs.

BackgroundPatients with medical information have become active participants in their treatment process; however, this has created additional challenges for physicians and other healthcare providers. However, how providers cope with patients’ requests for such information is not well understood, and the information-seeking behavior of physicians in catering to patients’ information needs has not been thoroughly studied. The primary objectives of this cross-sectional study were to understand healthcare providers’ information-seeking behavior when responding to patients’ specific questions about the benefits and risks of treatments.MethodologyThis Institutional Review Board-approved study was conducted at an independent academic center in Allentown, PA, between 2017 and 2020. We collected pertinent data from structured one-on-one interviews using an interview guide. The interviews were recorded using an electronic audio recorder that saved the recordings as an audio file. Interview transcripts were analyzed using hand coding. We investigated the relationship between categorical participant attributes using the chi-square or Fisher’s exact test at a significance level of 0.05, with continuity correction. We used the Kruskal-Wallis and Mann-Whitney U tests to investigate the differences between the distribution of continuous variables across the participants’ categorical attributes.ResultsA total of 124 providers from eight departments participated. The majority, 62% (77/124) of the providers, reported that patients brought information about treatment, and 56% (69/124) about a diagnosis. We did not notice a significant variation in the number of patients who brought in information related to their diagnosis (p = 0.08), prognosis (p = 0.35), and other topics, such as birth control, food allergies, and vaccines (p = 0.13), across medical specialties. Overall, 72% (89/124) of the providers reported using DynaMed, UpToDate, and Lexicomp, and 54% (67/124) used PubMed. Further, 27% (33/124) referenced the clinical practice guidelines, 17% (21/124) of the providers referred to textbooks, and 15% (19/124) discussed the topic with colleagues. Moreover, 35% (44/124) of the providers reported conducting traditional critical appraisals to determine the credibility of the information. We did not observe a significant variation in the number of providers using the library website to access journals and the PubMed database (p = 0.29) or in the number of providers seeking information from their colleagues (p = 0.58) across medical specialties. Providers who have recently finished their training (median experience = 6 years; range = 3-15) reported being not satisfied with their process of seeking information compared with providers with a greater level of experience (median = 15 years; range = 3-45) reporting being satisfied and (median = 14.5 years; range = 4-34) reporting being somewhat satisfied (p = 0.04).ConclusionsWe found that most physicians across all medical specialties utilized and preferred point-of-care tools, such as DynaMed and UpToDate. However, many providers still rely on the reputation of the information source, such as a journal’s impact factor and the author’s research credentials, to determine the credibility and reliability of the information.

Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using natural language, potentially mimicking the in-depth, inductive, and/or iterative process of qualitative analysis. We test the ability of generative LLMs to replicate and augment traditional qualitative coding, experimenting with multiple prompt structures across four closed- and open-source generative LLMs and proposing a workflow for conducting qualitative coding with generative LLMs. We find that LLMs can perform nearly as well as prior supervised machine learning models in accurately matching hand-coding output. Moreover, using generative LLMs as a natural language interlocutor closely replicates traditional qualitative methods, indicating their potential to transform the qualitative research process, despite ongoing challenges.

Mobile eye tracking technology enables the objective evaluation of what, when, and how athletes use visual information from the environment to control action in service of skilled performance. This review highlights the strengths, weaknesses, opportunities, and threats (SWOT) to consider before adopting this technology to evaluate visual–motor control for enhanced skill acquisition and performance in sport. Strengths include increased ecological validity by recording the athlete within their natural performance environment. Weaknesses include diminished tracking fidelity based on the eye’s morphology or squinting, eye tracker movement, and lens fogging. Opportunities consist of novel assessments around research themes such as combatting the negative effects of pressure and performance enhancement through personalized training. The formfitting technologies also introduce recording opportunities during standard practices. Data processing time, synchronization of video streams, and hand coding of variables comprise the threats in addition to the hardware cost and the fees associated with crowdsourcing data coding. Currently, it is also not feasible to provide coaches with a report of their athletes’ data in a timeline suitable to create player-specific drills. Evaluating portable eye tracking technology with a SWOT framework helps translate scientific knowledge to practitioners considering implementing this technology for athlete assessment and development.

This research investigates the potential of computational argumentation, specifically the application of the Abstract Argumentation Framework (AAF), to enhance the evaluation of deliberative quality in public discourse. It focuses on integrating AAF and its related semantics with the Discourse Quality Index (DQI), which is a reputable indicator of deliberative quality. The motivation is to overcome the DQI’s constraints using the AAF’s formal and logical features by addressing dependency on hand coding and attention to specific speech acts. This is done by exploring how the AAF can identify conflicts among arguments and assess the acceptability of different viewpoints, potentially leading to a more automated and objective evaluation of deliberative quality. A pilot study is conducted on the topic of abortion to illustrate the proposed methodology. The findings of this research demonstrate that AAF methods can improve discourse analysis by automatically identifying strong arguments through conflict resolution strategies. They also emphasise the potential of the proposed procedure to mitigate the dependence on manual coding and improve deliberation processes.

Abstract As relations between the United States and China have grown tenser, how has the Chinese Communist Party's (CCP) portrayal of the United States changed? And what might portrayals of the United States tell us about domestic messaging in China? This study systematically investigates CCP messaging about the United States in the contemporary era. To do this, we hand code, categorize and analyse 1,761 editorials about the United States published between 2003 and 2022 in People's Daily, the Party's flagship newspaper. In addition to showing a sustained rise in critical portrayals since 2018, we identify and elaborate three distinct critical narratives about the United States: it is a dangerous hegemon abroad, it has poor values at home, and it is increasingly weak and in decline. These narratives appear both independently and in combination and are often framed to contrast with portrayals of China. We argue that these narratives are not just negative propaganda to discredit the United States but can also be a strategy to promote a positive vision of the CCP's virtues and governance at home. This study contributes empirically and theoretically to research on propaganda and legitimation in China.

Though ideological differences have long been a ubiquitous feature of American politics, the rise of online news and social media has exacerbated divisions between groups. While existing research has documented how political preferences manifest online, relatively few studies have considered whether ideological divisions extend to discussions of foreign policy. We examine this question by analyzing nearly 2 million tweets about the war in Ukraine posted by Americans during the opening stages of the Russian invasion. We first categorize each tweet according to the user’s ideological leanings estimated by the network of political accounts they follow. Then, we apply a natural language processing model specifically designed for short texts to classify the tweets into clusters that we hand code into substantive topics. We find that the topic distributions of conservative, moderate, and liberal users are substantively and statistically different. We further find that conservatives are more likely to spread some form of misinformation and that liberals are more likely to express support for Ukraine. Our paper concludes with a discussion of the implications of our findings for the conduct of U.S. foreign policy.

Abstract We integrate the study of policy design and diffusion theoretically and empirically to advance each and raise novel research questions. We argue that policy dissemination is an important rationale in the design of model legislation and present a theory for how each of the five elements of design map onto the policy innovation attribute of complexity. Empirically, we both hand code policy design elements and use a novel textual approach to measure policy complexity across 84 model policies promulgated by the Uniform Law Commission, an interstate organization that writes model laws with input from 50 state delegations of lawmakers and disseminates the bills for passage in as many states as possible. We assess how the elements of policy design map onto a policy's textual complexity. Additionally, we find that more complex policies are less likely to be adopted and are slower to be adopted, though the strength of this relationship depends on the measurement approach. We conclude with the future directions of this research, including how new text analysis tools and large policy datasets can help to solve important measurement and analysis challenges.

Currently, continuous sign language recognition faces challenges such as difficulty in acquiring skeletal data, long training time for Three-Dimensional convolutional neural networks, and easy occlusion and blurring of hands. To address these problems, this paper proposes a continuous sign language recognition method based on target detection and coding sequence. The algorithm uses Dual-branch Shuffle Attention Mechanis-You Only Look Once version X (DSA-YOLOX) detection network to detect the head and hands, and encodes the sign language video according to the partition to achieve the transformation from Three-Dimensional to One-Dimensional; and then uses the proposed Bi-directional Long Short-Term Memory (BiLSTM) hand coding sequence classification model with jointly weighted Fast Dynamic Time Warping (FastDTW) to extract hand coding similarity and features while reducing the number of parameters to achieve the classification and recognition of unequal-length hand coding sequences. From the results of ablation experiments and comparison experiments, all parts of the improvement perform well. The word error rate (WER) of this paper’s method is reduced by 21.26% compared to Dynamic Time Warping-Hidden Markov Model (DTW-HMM) and 11.53% compared to Long Short-Term Memory-A(LSTM-A); the Giga Floating-point Operations Per Second(GFLOPs) of the algorithm are reduced dramatically, which is about 1/13 of the Visual Alignment Constraint(VAC) model and 1/57 of the Spatial-Temporal Multi-Cue(STMC) model; and the algorithm takes better account of the speed and accuracy of sign language recognition.

Objective: This study aims to the effect of role-play-based gamification on the analysis of symptoms in Internal Medicine Nursing, decision making, administration of appropriate nursing care, and the communication skills of nursing students. Methods: The study was designed in participatory action research as a qualitative design. In the action research, gamification elements were constructed on a role-play performance of the students in Nursing Department at a private university in Ankara-Türkiye. A gamification based on systems role play was performed for students taking the internal medicine nursing course (n=10). Checklists for each system regarding the selection and use of equipment appropriate for nursing interventions have been added to the games. At the end of the semester they were asked to evaluate this intervention via qualitative interview. In the in-depth interviews, data were collected by asking the students 9 unstructured open-ended questions prepared by the researchers. Data analysis was carried out by hand coding by the researchers. Codes were created from the research objectives based on Werbach's D6 Model for gamification design. Results: The qualitative analysis revealed six major themes (i) Defining Business Goals (ii) Delineating Target Behaviors (iii) Describing Players (iv) Devising Activity Loops (v) Deploying Tools (vi) Not forgetting the fun. In these themes, students stated that by using game elements in lessons, it became easier to remember and apply theoretical knowledge about internal medicine nursing, improved their practical skills, time management skills and critical decision-making abilities, and that they better understood the importance of effective communication within the team. Additionally, students stated that the experience of learning the basic aspects of internal diseases, which are difficult to learn in theory, became fun with the gamification technique. Conclusion: The use of gamification, students learned nursing care and communication skills related to Internal Medicine Nursing education in a motivated and more permanent manner.

Abstract Although implicit motives are well established, coding for implicit motives has been a time-consuming and difficult manual process. This study (i) evaluates automated motive coding schemes for Need for Achievement, Need for Affiliation, and Need for Power, (ii) assesses the relationship between these three motives and crisis initiation, and (iii) assesses the explanatory power of other psychological variables for crisis behavior. The results establish the convergent validity of the Need for Achievement and Need for Power coding schemes with hand coding and support findings of a significant relationship between Need for Power and crisis behavior. A unit increase in Need for Power doubles the probability that a leader will initiate a crisis. Even stronger models were obtained by including other previously studied psychological variables. The scope of the dataset (154 leaders across a diverse set of 93 countries) lends weight to the findings and provides many opportunities for additional research.

The authors describe protest patterns at U.S. and Canadian universities in the 2010s. The research draws on a new dataset, the Higher Ed Protest Event Dataset, which combines machine learning and sociological hand coding of 16,069 campus newspaper articles. The sample consists of 5,553 higher ed protests involving 584 universities and colleges between 2012 and 2018. The dataset also includes university and police responses to a subset of protests. The authors find that protest frequency is patterned by the academic calendar. The top issue in both U.S. and Canadian higher education protests was university administration and governance. The comparative analysis reveals distinctive patterns in other issues raised and protest intensity. In the United States, the periods of greatest protest activity were waves of mass mobilization across the country on often racialized issues with a national dimension: racist police violence, racially hostile campus climates, and Donald Trump’s presidency. In Canada, protest activity was most intense during provincial or local campaigns led by formal student organizations and unions on issues of economic security: public tuition, austerity, and labor conditions. Across both countries, university administrations and police usually avoided extensive intervention during protests. The findings contribute to social movements research through methodological innovations and new empirical insights on movements in higher education.

The spread of viruses and bacteria through touching door surfaces is essential in maintaining public hygiene and health. In this context, a hand-coded touchless automatic door hardware design has been developed to reduce the spread of diseases through touch. This research aims to create a plan that includes interface development and hardware design to open and close doors automatically without contact. In this research, the automatic door hardware response is tested based on the numeric input from the hand code represented by the numeric database. The input and output control is connected to Python's graphical user interface (GUI). The GUI system design involves tools to connect the Python programming language and the Arduino microcontroller. Based on the experimental results, the hardware design of the automatic door security system based on hand code and Convolutional Neural Networks functions appropriately.

Amaç: Bu nitel çalışmanın amacı, kadınların istenmeyen gebeliklerini sağlık kuruluşları dışında isteyerek düşükle sonlandırma sürecini betimlemektir. Yöntem: Çalışma grubunu, kartopu ve amaçlı örneklem yöntemi ile belirlenen, istenmeyen gebelik/isteyerek düşük deneyimi olan 13 kadın oluşturmuştur. Kadınlara yarı yapılandırılmış görüşme formu aracılığıyla düşük deneyimlerine ilişkin sorular derinlemesine görüşme yöntemi ile sorulmuştur. Görüşmelerden elde edilen veriler elle kodlama yapılarak kategorize edilmiş ve temalara ulaşılmıştır. Bulgular: Çalışmada, (i) kadınların sağlık hizmetlerinden etkili bir şekilde yararlanamadığı, (ii) bu konuda bilgi ve farkındalıklarının düşük olduğu, (iii) istenmeyen gebelikleri nedeniyle damgalanma korkusu yaşadıkları ve (iv) deneyimledikleri düşük/kürtaj sonrası sağlık sorunları yaşamalarına rağmen aynı koşullarda tekrar düşük/kürtaja başvurma riski taşıdıkları belirlenmiştir. Sonuç: Birinci basamakta ücretsiz ve ulaşılabilir olmasına rağmen aile planlaması hizmetlerinden kadınların etkili bir şekilde yararlanamadığı görülmektedir. Kadınlar aynı koşullarda tekrar düşük/kürtaja başvurma riski taşımaktadırlar. Bulgularımız, birinci basamak sağlık çalışanlarının kadınların aile planlaması hizmetlerine erişiminin önündeki engellerin farkında olması, doğurganlık çağındaki her kadını karşılanamayan aile planlaması gereksinimi yönünden değerlendirmesi ve istenmeyen gebelik yaşayan kadınların düşük/kürtaj hizmetine erken dönemde ve sağlıklı koşullarda erişimlerini sağlaması gerektiğini ortaya koymuştur.

Abstract We live in a world of text. Yet the sheer magnitude of social media data, coupled with a need to measure complex psychological constructs, has made this important source of data difficult to use. Researchers often engage in costly hand coding of thousands of texts using supervised techniques or rely on unsupervised techniques where the measurement of predefined constructs is difficult. We propose a novel approach that we call ‘super-unsupervised’ learning and demonstrate its usefulness by measuring the psychologically complex construct of online political hostility based on a large corpus of tweets. This approach accomplishes the feat by combining the best features of supervised and unsupervised learning techniques: measurements of complex psychological constructs without a single labelled data source. We first outline the approach before conducting a diverse series of tests that include: (i) face validity, (ii) convergent and discriminant validity, (iii) criterion validity, (iv) external validity, and (v) ecological validity.

Heated tobacco products (HTPs), including I Quit Ordinary Smoking (IQOS), are new tobacco products that use an electronic device to heat compressed tobacco leaves to generate an aerosol for consumers to inhale. Marketing of HTPs is prevalent on Instagram, a popular social media platform. This study aims to characterize posts related to HTPs on Instagram and their associations with user engagement. Through the Instagram application programming interface, 979 Instagram posts were collected using keywords related to HTPs, such as "IQOS" and "heat-not-burn." Among them, 596 posts were related to IQOS and other HTP marketing. The codebook was developed from a randomly selected 200 posts on the post content by hand coding, which was applied to the remaining 396 Instagram posts. Summary statistics were calculated, and statistical hypothesis testing was conducted to understand the popularity of Instagram posts on HTPs. Negative binomial regression models were applied to identify Instagram post characteristics associated with user engagement (eg, count). Among Instagram posts related to HTP marketing (N=596), "product display" was dominant (n=550, 92.28%), followed by "brand promotion" (n=41, 6.88%), and "others" (n=5, 0.84%). Among posts within "product display," "device only" was the most popular (n=338, 61.45%), followed by "heatstick only" (n=80, 14.55%), "accessory" (n=66, 12%), "device and heatstick" (n=56, 10.18%), and "capsule" (n=10, 1.82%). A univariate negative binomial regression model with pairwise comparisons across "product display" types showed that the number of likes for posts with HTP heatsticks was significantly lower compared to posts with HTP devices, accessories, and device-heatstick sets. Multivariate negative binomial regression models showed that HTP-related Instagram posts with a model or lifestyle elements (;=.60, 95% CI 0.36-0.84) or without obvious product advertising information (=.69, 95% CI 0.49-0.89) received more likes. It is shown that posts with product displays were dominant among HTP-related posts on Instagram. Posts with model or lifestyle elements are associated with high user engagement, which might be one of the web-based marketing strategies of HTPs.

Abstract Communication is a fundamental step in the process of political representation, and an influential stream of research hypothesizes that male and female politicians talk to their constituents in very different ways. To build the broad dataset necessary for this analysis, we harness the massive trove of communication by American politicians through Twitter. We adopt a supervised learning approach that begins with the hand coding of over 10,000 tweets and then use these to train machine learning algorithms to categorize the full corpus of over three million tweets sent by the lower house state legislators who were serving in the summer of 2017. Our results provide insights into politicians’ behavior and the consequence of women’s underrepresentation on what voters learn about legislative activity.

ABSTRACT Over the last 20 years Muslims have received a disproportionate amount of news media attention. In 2017 they were again thrust into the spotlight when the Trump administration issued a travel ban blocking entry from seven predominately-Muslim countries. With our hand coding of over 900 newspaper articles and mixed modeling techniques, we examine the newspaper rhetoric surrounding Muslims before and during the Trump administration and across US states. We find that after President Trump’s election, fewer newspapers mentioned religion or referenced Muslim claimsmakers. We also reveal that higher state-level religious salience was associated with more newspapers referencing religious claimsmakers in Islam-related discussions. Other state-level political and religious characteristics had no effect on the use of religious elements or Muslims claimsmakers. These results provide insight into the position of Muslims and Islam in the United States, as well as the impact of political rhetoric on media portrayals.

This study was aimed to investigate sign language skills of Kod Tangan Bahasa Malaysia (KTBM) as the early intervention program for learning sign language skills. KTBM introduced to ensure the communication of pupils with hearing impairments were introduced sign language skills hand code. This study was descriptive in the nature and data collected based on the qualitative approach paradigm. Semi-structured interviews conducted on four respondents, namely teachers and parents of the pupils with hearing impairments. The study showed that teachers argued that factors of sign language skills selected due to support the pupils in the teaching and learning process, easy-to-use sign language and parental fluency sign language skills. The interviewed findings presented on the controversy over the appropriate medium of instruction for pupils with hearing impairments is briefly discussed. This present study suggested that sign language training for the students with hearing impairments should given as early as possible before they were entered Year One to support the teaching and learning processes as well as more options of communication strategies for students with hearing impairments in the future.

Experiences of Women With Interstitial Cystitis/Bladder Pain Syndrome: What Can We Learn From Women's Online Discussions?