Narrow Set Research Articles

ScICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation

Clustering analysis is a fundamental step in scRNA-seq data analysis. However, its reliability is compromised by clustering inconsistency among trials due to stochastic processes in clustering algorithms. Despite efforts to obtain reliable and consensus clustering, existing methods cannot be applied to large scRNA-seq datasets due to high computational costs. Here, we develop the single-cell Inconsistency Clustering Estimator (scICE) to evaluate clustering consistency and provide consistent clustering results, achieving up to a 30-fold improvement in speed compared to conventional consensus clustering-based methods, such as multiK and chooseR. Application of scICE to 48 real and simulated scRNA-seq datasets, some with over 10,000 cells, successfully identifies all consistent clustering results, substantially narrowing the number of clusters to explore. By enabling the focus on a narrower set of more reliable candidate clusters, users can greatly reduce computational burden while generating more robust results.

Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning

The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-target toxicity. However, the current linker design space remains highly constrained, with most approved ADCs relying on a narrow set of established motifs. This limitation highlights an urgent need for computational tools capable of generating structurally diverse and synthetically accessible linkers. In this study, we introduce Linker-GPT, a Transformer-based deep learning framework leveraging self-attention mechanisms to generate novel ADC linkers with high structural diversity and synthetic feasibility. The model integrates transfer learning from large-scale molecular datasets and reinforcement learning (RL) to iteratively refine molecular properties such as drug-likeness and synthetic accessibility. During transfer learning, a pre-trained model was fine-tuned on a curated linker dataset, yielding molecules with high validity (0.894), novelty (0.997), and uniqueness (0.814 at 1k generation). RL further optimized the model to prioritize synthesizability and drug-like properties, resulting in 98.7% of generated molecules meeting target thresholds for QED (> 0.6), LogP (< 5), and synthetic accessibility score (SAS < 4). Linker-GPT demonstrates strong potential as a computational platform for accelerating the discovery and optimization of novel ADC linkers, offering a scalable solution for early-stage linker design. While these results are currently computational, they provide a foundation for future experimental validation and optimization.

Democratic innovation for change: A participatory corrective to deliberative hegemony

The world faces social, political, economic, and ecological crises, and there is doubt that democratic governance can cope. Democracies rely on a narrow set of institutions and processes anchored in dominant forms of political organisation and imagination. Power inequalities sustain the (re)production of current ills in democratic life. In this context, what does the field of democratic innovation offer to the task of sociopolitical reimagining and change? The field has advanced since the turn of the century, building foundations for democratic renewal. It draws from various traditions of democracy, including participatory and deliberative streams. But there is concern that a non-critical version of deliberative democracy is becoming hegemonic. Deliberative theory generated useful correctives to participatory democracy – that is, a deeper understanding of the communicative fabric of the public sphere as worthy of democratisation; public reasoning as a bridge-builder between streets and institutions and a key precursor to democratic collective action. However, we argue that democratic innovation now needs a participatory corrective to strengthen its potential to mobilise capacity for change. We review emerging critiques in conversation with participatory ideas and practices, illustrating our argument with four gaps in democratic innovation that can become field-expanding dimensions to deliver emancipatory change more effectively: pluriversality, policy, political economy, and empowerment.

A macroeconomic perspective on economic resilience and inclusive growth in the Philippines

here are at least two distinct but not equally important ways to understand what economic resilience means: one is focused on minimizing deviations of output about its trend and the quick return of output to trend following shocks, while another emphasizes the invariance of the underlying trend of output growth itself to shocks, including the ability to raise potential output despite shocks. The Philippine economy cannot be regarded as resilient using either definition. Anemic growth and the lack of economic resilience in the Philippines are primarily due to the inability of the government to make sufficient and quality investments in critical public goods such as climate change adaptation, health, education, and IT connectivity. The main reason for the lack of public (as well as private) investment is the presence of weak institutions and poor governance, characterized by a political economy process which provides many opportunities for rent-seeking behavior that benefit a narrow set of interests, and where adherence and sensitivity to the rule of law is lacking. Overcoming the problem of weak institutions and poor governance requires a change in the incentive structure faced by key institutions, with clear criteria and targets set and performance tied to tenure in office, so as to make government officials more accountable to the people. It requires a populace that demands accountability, transparency in motives and processes, and timely delivery of intended outcomes from the government, and an unwillingness to accept and trade off short-term token benefits for necessary investments to make growth robust, sustainable, and more inclusive. A well-informed and vigilant populace that demands adequate provision of quality public goods and services from the government is key.

Ethomics for Ecotoxicology: Automated Tracking Reveals Diverse Effects of Insecticides on Bumble Bee Foraging and In-Nest Behavior

Abstract The majority of flowering plants depend on insect pollination for reproduction and declining pollinator populations pose a threat to biodiversity as well as critical crop pollination services globally. Widespread insecticide use negatively impacts pollinator physiology and behavior even at environmentally-realistic concentrations below lethal toxicity, leading to reduced fitness and long-term population declines. However, significant gaps remain in our understanding of how insecticides affect diverse aspects of behavior and ultimately influence pollinator populations and pollination services. These gaps partly stem from the challenge of quantifying sublethal effects of pesticides on the complex behavioral repertoires of insects. Current methods often focus on a narrow set of behaviors at a time, limiting our ability to capture the comprehensive range of impacts within management-relevant timescales. The emergence of low-cost techniques for high-throughput behavioral quantification, or “ethomics”, holds enormous potential to address this knowledge gap. Here, we used automated, computer vision-based tracking implemented on open-source hardware (Raspberry Pis) to investigate the sublethal effects of an emerging “bee-safe” butenolide insecticide (flupyradifurone), as well as a neonicotinoid insecticide (imidacloprid), on bumble bee (Bombus impatiens) behavior. We simultaneously quantified the behavior of uniquely tagged individual workers both within the nest, and during foraging in a semi-field environment, to assess the holistic effects of insecticides under naturalistic conditions. Both insecticides increased mortality risk and altered behavior, but in distinct ways across behavioral contexts. Imidacloprid modified nest behavior by decreasing activity, while flupyradifurone altered spatial behavior within the nest (shifting bees toward the brood). Imidacloprid—but not flupyradifurone—reduced overall foraging activity, while both affected floral preference. Overall, our results highlight the complex potential mechanistic links between sublethal insecticide exposure, behavior, and pollinator health. This work emphasizes the need—and possibility—for rapid and holistic pesticide risk assessment under realistic environmental conditions using high-throughput ethomics, and could inform the development of sustainable agricultural practices and conservation strategies.

Exploring the Composition of Forest Collaboratives in Northeastern Oregon

Community-based collaboration has been touted as an effective model for forest governance because it promotes democratized decision-making and stakeholder engagement to address landscape-scale problems. Forest collaboratives are assumed to be heterogeneous, consisting of stakeholders with a diverse range of interests. Few studies have systematically explored variables associated with collaborative composition. We identify six elements of collaborative composition for investigation: size, stakeholder diversity, balance, locality–diversity, core attendance, and cross-participation. This exploratory study examines five forest collaborative groups in eastern Oregon (USA). We analyzed meeting minutes over an 18-month period to track attendance and evaluate who shows up and at what frequency. While forest collaboratives vary in size, larger collaboratives are more heterogeneous, reflecting greater diversity in terms of stakeholders represented, and have a higher proportion of high-frequency (‘core’) attendees. Core attendees and attendees who participated across multiple forest collaboratives regionwide represent a much narrower set of stakeholder interests. Collaboratives’ attendees reflected a mix of local and non-local organizations. The results raise questions about whether collaborative groups represent the array of public interests in planning for forest management.

Nanotubular Gradients on Titanium: High-Throughput Screening of Nanoscale Architectures of Variable Topographical Complexity.

Advancements in cell-instructive biomaterials hinge on the precise design of their nanoscale topography, a critical factor in controlling cell-surface interactions. Nanofabrication techniques such as e-beam and nanoimprint lithography enable accurate nanopatterning on a wide range of materials. However, their limited applicability and scalability to medically relevant metals such as titanium, hinder the creation and modulation of precisely designed nanotopographies on metallic substrates to investigate structure-function relationships and clinical translation of nanotopographical surfaces for biomedical implants. In this context, anodization is a cost-effective, scalable method to nanopattern titanium and its alloys, producing arrays of TiO2 nanotubes with precisely controlled diameters. Despite the significant advances in the understanding of how cells sense and respond to nanotubular surfaces, traditional diameter-focused research reliant on single-sized nanostructures restricts analysis to a narrow set of geometrical parameters and often overlook the spatial arrangement of nanotubes. To address these limitations, this study capitalizes on anodization to create scalable nanotubular gradients on titanium, introducing a high-throughput platform to explore the cellular response to a wide range of nanotopographical configurations within a single sample. Utilizing spatial metrics such as lacunarity, entropy, and fractal dimension, we characterized the structural complexity of the nanotubular surfaces, emphasizing geometrical considerations beyond the nanotube diameter in evaluating cellular response. In vitro assays with human MG63 osteoblastic cells revealed that more disordered, high-entropy regions significantly enhance cellular spreading and proliferation while promoting early osteogenic differentiation, evidenced by elevated RUNX2 and osteocalcin (OCN) expression. In contrast, mitochondrial activation and longer-term mineral deposition are elicited by more ordered nanotubular arrays. By streamlining the screening of nanotopographical features and enabling reproduction of user-selected designs as homogeneous surfaces, this gradient-based approach deepens mechanistic insights into structure-function relationships governing MG63 cell response to anodized titanium and offers a translatable framework for designing and evaluating nanotubular surfaces, shortening the gap between in vitro research and clinical applications.

Geographic representation in parliamentary questions: the benefits of Wikipedia for automated analyses of legislative text

ABSTRACT There is a growing interest in the representation of electoral districts in non-binding legislative instruments such as parliamentary questions. While early research has mostly relied on labor-intensive manual coding, scholars have increasingly turned to automated text analyses. So far, these efforts have been limited by narrow or case-specific sets of keywords, which have not yet been tested for reliability. As a step forward, this research note discusses two data sources for the comparative study of geographic representation in parliamentary text. Based on a case study of parliamentary questioning in Germany, France, and the UK, the paper concludes that Wikipedia is a promising candidate for identifying geographic representation in parliamentary text.

Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data

Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R2 values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management.

Time for a paradigm shift? Exploring competition regulation and its relationship with the rising global burden of industrial epidemics.

Competition regulation plays a key role in determining firm size, market structure, and what firms can do with their market power. In this paper, we explore how competition regulation in many countries has largely tolerated rising industry concentration and market power in harmful consumer product industries, which, in turn, has likely facilitated an increase in preventable death and disease associated with such industries (ie. industrial epidemics). One important reason for this tolerance has been the rise of the 'consumer welfare' standard, which contends that competition regulators should only focus on a narrow set of concerns mostly relating to consumer price and output. Yet, recent developments shed light on potential avenues through which competition regulation could work more synergistically with public health policies and programmes. While discussions on how to leverage competition regulation along these lines are invariably contested and complex, we argue that it is critical that public health advocates engage with these discussions.

The use of international classification of diseases codes to identify hospital admissions linked with adverse drug events: Validation study.

Several methods exist to identify hospital admissions related to adverse drug events (ADEs). Clinical adjudication by healthcare professionals is the gold standard but is labour-intensive. Spontaneous reporting and routinely collected healthcare data using a set of International Classification of Diseases (ICD) codes often underestimate the prevalence of ADE-related admissions. Expanding the set of ICD codes could improve detection; however, validation is limited. The objective was to describe the agreement between ADE-related ICD-10 codes and clinically adjudicated ADE-related admissions in 2 settings. This study analysed 2 datasets: 1102 readmissions from a hospital in the Netherlands (180 ADE-related) and 1228 admissions from a hospital in the Czech Republic (195 ADE-related). Clinical adjudication involved expert review including causality assessment to identify ADE-related hospital admissions. The sensitivities and specificities were calculated for a narrow code set (higher drug-likelihood codes containing words like drug-induced) and a broad code set of ICD-10 codes (including codes very likely, likely and possibly ADE-related). The narrow ICD-10 set showed a sensitivity of 3% (95% confidence interval [CI] 2-6%) and a specificity of 99.6% (95% CI 99-100%). The broad set increased sensitivity to 27% (95% CI 23-32%), with specificity decreasing slightly to 92% (95% CI 91-94%). Preventable ADEs were identified less frequently with both ICD-10 code sets. Only 3% of ADE-related admissions were detected by the narrow ICD-code set and 27% by the broad code set without a significant drop in the specificity. ADE-related ICD codes seem to serve as triggers for 1 in 4 ADE-related hospital admissions.

DEVELOPMENT OF A TERNARY LEVELS EMOTION CLASSIFICATION MODEL UTILIZING ELECTROENCEPHALOGRAPHY DATA SET

Electroencephalogram (EEG)-based emotion recognition has gained increasing attention due to its potential in objectively assessing affective states. However, many existing studies rely on limited datasets and focus on binary classification or narrow feature sets, limiting the granularity and generalizability of their findings. To address these challenges, this study explores a ternary classification framework for both valence and arousal dimensions—dividing each into low, medium, and high levels—to capture a broader spectrum of emotional responses. EEG recordings from ten randomly selected participants in the DEAP dataset were used. Each 60-second EEG segment was divided into six non-overlapping windows of 10 seconds to preserve temporal stability and extract reliable features. The Hilbert Transform was applied to compute instantaneous amplitude and phase information, enabling the detection of subtle variations in emotional states. These features were then classified using a feed-forward neural network. The proposed approach achieved impressive classification accuracies of 99.13% for arousal and 99.50% for valence, demonstrating its effectiveness in recognizing multi-level emotional states. By moving beyond binary labels and leveraging time-frequency domain features, this study contributes to the development of more refined and responsive emotion recognition systems. These findings offer promising insights for real-world applications in affective computing, mental health monitoring, and adaptive human-computer interaction, where precise emotion modeling plays a critical role.

Sequential Sovereignty between Authenticity and Justice

In this paper I argue that in his otherwise important theory of sequential sovereignty accorded to an intergenerational people Alessandro Ferrara unduly privileges the founding generation. For him this first generation is the only one to have the constituent power to create a demos by accepting specific identity-defining commitments in light of an already given ethnic identity. With regard to the amending power of future generations these constitutional essentials determine what can and cannot count as authentic expressions of this demos going forward in time. However, Ferrara goes wrong when he includes specific institutional designs in these identity-defining commitments. Rather, I maintain that the idea of intergenerational justice requires that the founding generation restricts itself to a much narrower set of constitutional essentials. Fundamental commitments that are hard to amend or cannot be amended at all should only include those features that form the necessary normative core of the liberal political project: namely to cooperatively share a political order on fair terms no one can reasonably reject. This institutionally more open-ended proposal is better in line with what non-overlapping generations owe to each other as equals with regard to their joint cooperative project.

How Does CMS' Merit-based Incentive Payment System Penalize or Reward Orthopaedic Surgeons Caring for Socially At-risk Patients?

Background The largest value-based payment system in the United States is the Merit-based Incentive Payment System (MIPS), implemented by the Centers for Medicare &amp; Medicaid Services (CMS). MIPS was designed to adjust physician reimbursement based on performance across several categories. However, concerns arose that MIPS may inadvertently penalize physicians caring for patients of high social risk. To address this concern, CMS introduced the Complex Patient Bonus (CPB), which provides a performance bonus for serving a greater proportion of dually eligible, or socially at-risk (as defined by CMS), patients. In orthopaedic surgery, there is a paucity of evidence assessing MIPS performance (such as scores and payment adjustments), the association between patient social risk and MIPS scores, and the relationship of the newly implemented CPB with performance scores. Questions/purposes In this study, we asked: (1) How do orthopaedic surgeons fare in MIPS based on positive, negative, and bonus payment adjustments? (2) Do orthopaedic surgeons caring for more socially at-risk patients receive worse performance scores and payment adjustments than orthopaedic surgeons who treat fewer socially at-risk patients? (3) To what extent is the CPB associated with differences in MIPS scores and payment adjustments for orthopaedic surgeons caring for a greater proportion of socially at-risk patients? Methods Orthopaedic surgeons participating in MIPS in 2021 were identified using publicly available, nationally representative, standardized CMS data sets, consistent with prior studies assessing clinician performance under MIPS. In keeping with prior studies and consistent with how CMS defines social risk for the purpose of adjusting MIPS performance and payments using the CPB, dual eligibility for Medicare and Medicaid was used as a proxy for social risk. Surgeons were stratified into quintiles based on the proportion of patients dually eligible for Medicare and Medicaid. To answer the first question about how orthopaedic surgeons, in aggregate, perform in MIPS, CMS MIPS outcome data were used to quantify the proportion of surgeons who received a positive or negative payment adjustment, an exceptional performance bonus, and a maximum payment penalty. To address the second question regarding the association between caring for socially at-risk patients and MIPS performance, MIPS scores and payment adjustments were compared between surgeons in the highest and lowest quintiles of patient social risk, as determined by the proportion of dually eligible patients in each surgeon’s practice per CMS definition. To evaluate the extent to which the CPB is associated with differences in MIPS performance, multivariable regression was used to assess whether the proportion of socially at-risk patients in a surgeon’s practice was associated with differences in MIPS scores, payment adjustments, and exceptional performance bonuses, with and without the CPB. Results Regarding how orthopaedic surgeons performed in MIPS, 97% (9415 of 9707) of orthopaedic surgeons in the study received a positive payment adjustment, and 0.5% (50 of 9707) received the maximum penalty. When comparing surgeons caring for more socially at-risk patients with those caring for fewer (mean ± SD proportion of dual eligible patients 31% ± 11% versus 2% ± 2%; p &lt; 0.001), surgeons in the highest social risk quintile achieved higher MIPS scores (with CPB 94 versus 91, p &lt; 0.001; without CPB 90 versus 88, p = 0.001). However, no difference in payment adjustments was observed between surgeons caring for the highest and lowest proportion of socially at-risk patients (lowest quintile, any positive MIPS score adjustment 96% [1872 of 1943] versus highest quintile, any positive MIPS score adjustment 96% [1870 of 1942]; p = 0.93). In examining the role of the CPB, caring for a higher proportion of socially at-risk patients was associated with a higher MIPS score with the CPB (β 1.9 [95% confidence interval (CI) 0.51 to 3.20]; p = 0.007), but not without the CPB (β 0.6 [95% CI -0.79 to 2.02]; p = 0.39). No association was found between the proportion of socially at-risk patients cared for and receipt of an exceptional performance bonus (odds ratio [OR] 1.3 [95% CI 0.95 to 1.72]; p = 0.10) or positive payment adjustment (OR: 0.8 [95% CI 0.46 to 1.34]; p = 0.37). Conclusion Our findings highlight potential disconnect between MIPS performance and financial implications, particularly for surgeons treating more socially at-risk patients. The lack of differentiation in performance outcomes, evidenced by nearly all participating surgeons receiving a positive adjustment in a budget-neutral program, raises concerns about how MIPS measures and rewards performance. As new value-based payment models continue to be introduced, including those with greater downside or variation in payment adjustments, ensuring appropriate risk-adjustment is crucial to their success and achieving buy-in from practicing orthopaedic surgeons. For orthopaedic surgeons, these findings may contextualize their MIPS performance, clarify the limited role that payment adjustments play in recognizing surgeons who care for more complex or socially at-risk patients, and inform how they engage with institutional quality initiatives or advocate for more meaningful, clinically oriented performance measures. Future studies should evaluate whether a narrower set of episode-based, patient-centric metrics may better reflect the quality of surgical care provided and support outcome-focused value-based payment models. Level of Evidence Level III, therapeutic study.

Review and Publication Times and Reporting Across Journals on Health Policy

This cross-sectional study compares how a broad range of journals vs a narrow set of specialty journals report and measure review and publication times and assesses these patterns across journals.

Optimizing arm placement in the Hugo™ RAS system-based hysterectomy: development and validation of a simplified "Narrow setting" approach.

We aimed to determine the usefulness of the new setup, the "Narrow setting," by examining our initial experience with the Hugo™ RAS system. 78 hysterectomies using the Hugo™ RAS system performed in "Narrow setting" at our institution from November 2023 to December 2024 were included in this study. We retrospectively examined the patient's clinical information and surgical outcomes from the medical record. We also investigated the learning curve of docking time in this setting by cumulative summation analysis. The median age, body mass index, and uterine weight of the patients were 48 (31-76) years, 22.9kg/m2 (16.1-42.4), and 178g (40-2000g), respectively. More than half of the surgical indications were uterine myomas (52.6%, 41/78). The median operative, console, and docking times were 68min (48-198min), 46min (29-151min), and 9min (6-31min), respectively. The median estimated blood loss was 5mL (5-220mL). A total of eight perioperative complications were observed, but only one was classified as Clavien-Dindo grade III or higher. No conversion to open or laparoscopic surgery was required. The learning curve for docking time showed that docking in the "Narrow setting" can be proficient in 19 cases. We reported on our initial experience with hysterectomy using the Hugo™ RAS system and found the "Narrow setting" to be safe and efficient.

Metrics used in quality improvement publications addressing environmental sustainability in healthcare: a scoping review.

Quality improvement (QI) practices and scholarship are increasingly concerned with environmental sustainability given the negative health outcomes caused by the ecological crisis, as well as the environmental impacts of healthcare delivery itself. A core component of QI activities is measuring change. How sustainability metrics have been used in QI is unclear. We conducted a scoping review of metrics used in published sustainability-focused QI initiatives. MEDLINE, EMBASE, CINAHL and Scopus from 2000 to 2023. Published healthcare QI initiatives intended to address environmental sustainability with at least one quantitative sustainability metric. Publication, study, measurement and QI intervention characteristics were charted from included studies. Data items were synthesised and presented narratively as well as quantitatively. We screened 6294 studies and included 90 full-text publications. The studies were published from 2000 to 2023, with the majority (61%, 55/90) published since 2020. Publications originated from a wide range of clinical disciplines with most QI projects situated in the inpatient setting (78%, 70/90). Environmental sustainability metrics were subcategorised into activity data and environmental impact indicators. Some papers included more than one category of activity data, with the most common being cost (88%, 79/90), hospital waste (52%, 47/90), anaesthetic gases (49%, 44/90), disposable use (24%, 22/90) and distance travelled (14%, 13/90). Fewer publications included environmental impact indicators, with global warming potential dominating this category (53%, 48/90). There is a need to align QI efforts with environmental sustainability. However, there is limited guidance specific to healthcare QI on how to measure environmental impacts of these efforts. This review illuminates that sustainability-focused QI efforts to date have used a relatively narrow set of sustainability metrics. QI scholars and practitioners can benefit from further education, measurement frameworks and guidelines to effectively incorporate environmental sustainability metrics into QI efforts.

Affect, Efficacy, and Protest Intentions: Testing a Multilevel, Dual-Pathway Model of Collective Action

What are the consequences of affect and efficacy for protest intentions, and do these consequences stem from personal disposition and/or situational characteristics? Here, we test a dual-pathway model of collective action in which anger and efficacy operate at multiple levels of analysis. To test this model, we administer a factorial survey experiment of student protest to a random sample of undergraduate students (N = 880). We find that the effect of anger on protest intentions follows two routes—one dispositional and one situational—and that the effect of efficacy flows through a situational channel. We also find that anger and efficacy are triggered by a broad set of situational conditions (incidental grievances, selective rewards and punishments, collective action frames, and protest size) and that anger is also a function of a narrow set of dispositional factors (protest attitudes). Taken together, our findings support a multilevel, dual-pathway model of collective action.

Crop biocultural traits and diversity dynamics among Bassari farmers

Abstract Cultural and ecological dimensions of agriculture are often considered as contrasting in agricultural research. This is well reflected on approaches to variety evaluation and selection that privilege a narrow set of agronomic indicators that do not account for the complexity of farmer-crop interactions. In this work, we explore the concept of ‘crop biocultural traits’ to integrate the social and biological dimensions of crops and the entanglements between them. Our research is based on a case-study in a Bassari village of south-eastern Senegal, where we explored the biocultural traits that farmers assign to crops and varieties together with their abundance, distribution and trends. We focus on six local staple crops, namely sorghum, Bambara groundnut, fonio, maize, rice and peanut. Our methods include key-informant and semi-structured interviews, individual trait scoring exercises and participatory workshops. Our results reveal that Bassari farmers characterize crops and varieties considering both their agronomic but also their socio-economic and cultural traits. Bassari maintain a basket of crops and varieties that, together, bear multiple and complementary traits. However, no biocultural trait alone can explain crop and variety abundance, distribution, and trends. We conclude that understanding crop diversity dynamics requires embracing the complexity of biocultural interactions. We argue that this is also a matter of ontological pluralism and of viewing agricultural knowledge as a collective effort and a common good. Only by including diverse ways of knowing will it be possible for plant breeding and conservation efforts to address farmers contextualized needs and priorities.

Hegemony and International Alignment

This article explores the interplay between economic hegemony and political alignment. Using theoretical and empirical insights from Broner et al. (2024), we posit that hegemonic states, like the United States, foster political alignment, which enhances globalization. We use UN voting data to proxy for international alignment and show that hegemons induce alignment. These data have shortcomings, however. UN voting only covers the post-WWII period, refers to a narrow set of issues, and displays little time variation. As for military alliances, they were not widely used before the mid-twentieth century. We propose an alternative measure of alignment based on international treaties.