Robustness Considerations Research Articles

Enhancing the global and local robustness of networks: A network motif-based approach

In the real world, both natural and artificial networks, may experience degradation or failure due to faults or external perturbations, which may negatively impact people's lives. Therefore, optimizing network robustness to maintain functionality under such conditions is a crucial research area. With the deepening of network robustness research, more and more studies have found that the local robustness of networks is related to network motifs. Considering the optimization of the robustness based on the global network properties (global robustness) and local robustness of the network, this paper proposes a motif-based edge rewiring algorithm (MBER), which includes the Edge Selection Algorithm, Edge Rewiring Algorithm, Simplified Calculation Algorithm for the Number of Major Network Motifs and Adjusted Nondominated Sorting Algorithm. We optimize four synthetic networks and four real-world networks using MBER and four other optimization algorithms, respectively. The results demonstrate that the MBER algorithm is more effective in enhancing the global and local robustness of networks. Additionally, we analyse the time complexity of MBER and four other optimization algorithms, revealing that the MBER algorithm exhibits the highest time complexity due to its consideration of local robustness. This paper can provide a reference for enhancing the robustness of real-world networks more comprehensively and enriches the research on network robustness enhancement.

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Trade and socioeconomic importance of an invasive giant snail in the endemic-rich island of São Tomé, Central Africa.

Managing invasive species is crucial to mitigate their negative impacts on ecosystems, yet conflicts may arise when their social benefits are disregarded. Human pressure on the endemic-rich forests of São Tomé has been high since the island was discovered by the Portuguese in the 15th century, and numerous species have been introduced. These include the invasive West African giant land snail (Archachatina marginata), which was introduced in the mid-20th century, is now widespread on the island, and is a potential threat to native flora and fauna. We assessed the frequency of consumption of this species and its socioeconomic importance to people across the island with household questionnaires, focus group discussions, and semistructured interviews. We explored the prevalence and potential drivers of use (e.g., wealth, household composition, and diversity of occupations) and characterized the commodity chain to identify demographic groups linked to the snail trade. We interviewed 672 people (1 person per household), conducted 6 focus groups, and interviewed 80 key actors belonging to 5 subcategories. The snail was the most widely consumed bushmeat and an important source of income, particularly for women and unemployed youth. Insecure and scarce livelihood alternatives, mostly in rural areas, were reported as drivers for trade involvement. Snail harvesting was more frequent in poorer households with low occupational diversity. Selling tended to occur in households that were well-established in the community and had a higher proportion of children. Both were stimulated by the proximity of communities to the native forest. Buying snails was common in all demographic groups, but it was linked to wealth and occupational diversity. Interventions to manage the impact of this introduced species on the valuable ecosystems of the island should involve multiple sectors of society to ensure societal support. This requires robust consideration of the welfare of vulnerable demographic groups that benefit from the species.

Explicit consideration of geomaterial uncertainties in the load resistance factor design of piles driven into rock-based intermediate geomaterials

The study presents a method for estimating the end bearing of piles driven into rock-based intermediate geomaterials (IGMs). Existing design methods, rooted in soil mechanics, often fall short when applied to IGMs. Moreover, the geomaterial (geological and ground properties) uncertainties are neglected and the piles are designed based on subsurface information from distant boreholes. To address this gap, the study introduces an approach that explicitly considers geomaterial uncertainties by combining empirical models and geostatistical simulation to predict end bearing. Using data from 87 piles driven into various rock-based IGMs with dynamic load testing as the construction control technique, this study introduces the Proposed Design Method (PDM) for the prediction of end bearing. PDM is compared to the Current Design Method (CDM), which is based on traditional soil-mechanics methods. Predictions from these methods are subsequently evaluated against the measured end bearings. The results demonstrate that while the PDM yields comparable predictions to the CDM, it offers a more robust consideration of geomaterial uncertainties. The PDM leads to an approximate 13.60% to 19.35% increase in uncertainties compared to CDM across various IGMs. These findings advance the understanding of pile behavior in the presence of geomaterial uncertainties and inherent variability.

Tractography from T1-weighted MRI: empirically exploring the clinical viability of streamline propagation without diffusion MRI

Abstract Over the last few decades, diffusion MRI (dMRI) streamline tractography has emerged as the dominant method for in vivo estimation of white matter (WM) pathways in the brain. One key limitation to this technique is that modern tractography implementations require high angular resolution diffusion imaging (HARDI). However, HARDI can be difficult to collect clinically, limiting the reach of tractography analyses to research cohorts and thus limiting many WM investigations to certain populations and pathologies. As such, a clinically viable tractography solution applicable to wider patient populations scanned as a part of routine care would be of key significance in broadening WM analyses to underfunded or rarer diseases and to the clinical setting. Such a solution would require the ability to perform arbitrary tractography analyses, use only clinical imaging for input, and be open source and widely accessible and implementable. Thus, here we evaluate our recently developed, containerized and open-source, T1-weighted (T1w) MRI-based deep learning model for streamline propagation. We empirically assess its performance against traditional dMRI-based and established atlas-based approaches in a healthy young population, an aging one, and in those with epilepsy, depression, and brain cancer. In the healthy young population, we find slightly increased error compared to traditional tractography with the deep learning model that falls within the bounds attributable to dMRI variability and is considerably less than the atlas-based approach. Further, seeking to replicate previously published dMRI tractography effects in the remaining cohorts as an initial assessment of clinical viability, we find this model successfully does so in some key cases—particularly in applications that rely on long-range streamlines including those not captured by the atlas-based approach—but importantly not all. These results suggest a deep learning-based approach to tractography with T1w MRI demonstrates promise within the limitations of our definition of clinical viability and especially over atlas-based approaches but requires refinement and more robust consideration of out-of-distribution effects prior to widespread clinical use. We also find these results raise additional questions regarding the differences in image content between dMRI and T1w MRI and their relationship to tractography. Further investigation of these questions will improve the field’s understanding of which features of the brain influence measured tractography effects.

Knowledge correction and [formula omitted]-insensitive criterion-leveraged zero-order TSK fuzzy system for rice leaf disease diagnosis

The advent of complex application scenarios introduces new challenges for diagnosing rice leaf diseases using machine learning methods. Two critical requirements are identified: 1) The model must exhibit high interpretability to mitigate the adverse effects of incorrect diagnoses; and 2) practical applications often suffer from insufficient samples and noise in rice leaf disease datasets, which requires the model to have strong generalization ability and robustness. However, existing methods still have certain limitations in practical scenarios due to a lack of comprehensive consideration of interpretability, generalization ability, and robustness. To address this issue, this article proposes a novel knowledge correction and ε-insensitive criterion-leveraged zero-order TSK fuzzy system (0-TSK-FS), named KE-0-TSK-FS. The KE-0-TSK-FS method is developed with 0-TSK-FS as the baseline, enhancing the generalization ability of the model by introducing the knowledge correction method and its iterative learning strategy to extract more information from limited samples. In addition, the objective function based on the ε-insensitive criterion makes KE-0-TSK-FS exhibit robustness when the samples contain noise. On three rice leaf disease datasets and six real-world non-rice leaf disease datasets, experiments were conducted on three metrics, namely accuracy, GM, and rule complexity. The experimental results show that the KE-0-TSK-FS method outperforms other comparative algorithms in terms of generalization ability, interpretability, and robustness in the diagnosis of rice leaf diseases under insufficient samples and noise situations, and its average accuracy on rice leaf disease datasets is nearly 3% higher than that of other comparative algorithms.

Robust knowledge distillation based on feature variance against backdoored teacher model

Benefiting from large well-trained deep neural networks (DNNs), model compression has captured special attention for computing resource limited equipment, especially edge devices. Knowledge distillation (KD) is one of the widely used compression techniques for edge deployment, by obtaining a lightweight student model from a well-trained teacher model released on public platforms. However, it has been empirically noticed that the backdoor in the teacher model will be transferred to the student model during the process of KD. Although numerous KD methods have been proposed, most of them focus on the distillation of a high-performing student model without robustness consideration. Besides, some research adopts KD techniques as effective backdoor mitigation tools, but they fail to perform model compression at the same time. Consequently, it is still an open problem to well achieve two objectives of robust KD, i.e., student model’s performance and backdoor mitigation. To address these issues, we propose RobustKD, a robust knowledge distillation that compresses the model while mitigating backdoor based on feature variance. Specifically, RobustKD distinguishes the previous works in three key aspects: (1) effectiveness - by distilling the feature map of the teacher model after detoxification, the main task performance of the student model is comparable to that of the teacher model; (2) robustness - by reducing the characteristic variance between the teacher model and the student model, it mitigates the backdoor of the student model under backdoored teacher model scenario; (3) generic - RobustKD still has good performance in the face of multiple data models (e.g., WRN 28-4, Pyramid-200) and diverse DNNs (e.g., ResNet50, MobileNet). Comprehensive experiments are conducted on four datasets, six models, two distillation methods, and two backdoor attack methods, compared with four baselines, and the results verified that the proposed method achieves the state-of-the-art performance in both aspects of accuracy and robustness. In addition, RobustKD is still effective when adaptive attacks are considered. The code of RobustKD is open-sourced at https://github.com/Xming-Z/RobustKD.

Adversarial Robust Safeguard for Evading Deep Facial Manipulation

The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur. In addition, traditional optimization-based methods face limitations in scalability as they struggle to accommodate the substantial expansion of data volume, a consequence of the time-intensive iterative pipeline. To solve these challenges, we propose a learning-based model, Adversarial Robust Safeguard (ARS), to generate desirable protection noise in a single forward process, concurrently exhibiting a heightened resistance against prevalent perturbations. Specifically, our method involves a two-way protection design, characterized by a basic protection component responsible for generating efficacious noise features, coupled with robust protection for further enhancement. In robust protection, we first fuse image features with spatially duplicated noise embedding, thereby accounting for inherent information redundancy. Subsequently, a combination comprising a differentiable perturbation module and an adversarial network is devised to simulate potential information degradation during the training process. To evaluate it, we conduct experiments on four manipulation methods and compare recent works comprehensively. The results of our method exhibit good visual effects with pronounced robustness against varied perturbations at different levels.

Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for asynchronous cross-domain federated learning scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

Influence of Cross-Correlation on the Modelled Uncertainty in Stress–Strain Behavior of Soft Clays

Reliability-based design is increasingly being applied to geotechnical problems because it allows the robust consideration of various sources of uncertainty, such as the inherent variability of soil properties. Some soil properties, however, are mutually dependent, and ignoring this cross-correlation may lead to biased estimates of the probability of unsatisfactory performance. Hence, in this study, the Gaussian copula was used to evaluate how the applied cross-correlation or independence between the compressibility properties affects the uncertainty in the stress–strain response of two marine soft clays. Two settlement calculation methods were considered: the compression index and Janbu tangential stiffness methods. The correlation coefficients were defined from the site-specific oedometer data at two extensively studied clay sites, and from a database. The simulated oedometer curves were compared to the observed variability in the site-specific data. The settlements in the clay sublayers were then computed, and different cases were compared by means of boxplots. It is concluded that the Janbu method leads to a significant overestimation of uncertainty in settlement if the cross-correlation between the compressibility parameters is ignored. On contrary, the compression index method seems less sensitive to the assumed correlation structure, and as such, the parameters can be treated as independent in most cases.

Development of patient and catheter specific error thresholds for high dose rate prostate brachytherapy.

In-vivo source tracking has been an active topic of research in the field of high-dose rate brachytherapy in recent years to verify accuracy in treatment delivery. Although detection systems for source tracking are being developed, the allowable threshold of treatment error is still unknown and is likely patient-specific due to anatomy and planning variation. The purpose of this study was to determine patient and catheter-specific shift error thresholds for in-vivo source tracking during high-dose-rate prostate brachytherapy (HDRPBT). A module was developed in the previously described graphical processor unit multi-criteria optimization (gMCO) algorithm. The module generates systematic catheter shift errors retrospectively into HDRPBT treatment plans, performed on 50 patients. The catheter shift model iterates through the number of catheters shifted in the plan (from 1 to all catheters), the direction of shift (superior, inferior, medial, lateral, cranial, and caudal), and the magnitude of catheter shift (1-6mm). For each combination of these parameters, 200 error plans were generated, randomly selecting the catheters in the plan to shift. After shifts were applied, dose volume histogram (DVH) parameters were re-calculated. Catheter shift thresholds were then derived based on plans where DVH parameters were clinically unacceptable (prostate V100<95%, urethra D0.1cc>118%, and rectum Dmax>80%). Catheter thresholds were also Pearson correlated to catheter robustness values. Patient-specific thresholds varied between 1 to 6mm for all organs, in all shift directions. Overall, patient-specific thresholds typically decrease with an increasing number of catheters shifted. Anterior and inferior directions were less sensitive than other directions. Pearson's correlation test showed a strong correlation between catheter robustness and catheter thresholds for the rectum and urethra, with correlation values of -0.81 and -0.74, respectively (p<0.01), but no correlation was found for the prostate. It was possible to determine thresholds for each patient, with thresholds showing dependence on shift direction, and number of catheters shifted. Not every catheter combination is explorable, however, this study shows the feasibility to determine patient-specific thresholds for clinical application. The correlation of patient-specific thresholds with the equivalent robustness value indicated the need for robustness consideration during plan optimization and treatment planning.

International Effect: The Ongoing Tension with Medical Marijuana Legalization

Purpose: In the landscape of the United States, an escalating number of states are ushering in a new era by enacting legislation that affords individuals the right to employ cannabis for medical purposes. However, this commendable stride toward medical autonomy is accompanied by an escalating tension, a discordant symphony of conflict between state and federal jurisdictions. This study embarks on a journey through the labyrinth of legislative intricacies, seeking to unravel the roots of this tension and proffering insights into potential resolutions. At the heart of the matter lies the dichotomy between state and federal legislation. While states pave the way for medical cannabis use, the federal stance casts a looming shadow of ambiguity and discord. The conflict, multifaceted and dynamic, beckons for a nuanced exploration.&#x0D; Methodology: The study adopted desktop literature research design.&#x0D; Findings: By delving into the intricacies of the legislative measures themselves, this research identifies mitigating factors that can potentially alleviate the tension between conflicting jurisdictions. Mitigation, however, requires more than a superficial understanding of legislative nuances. It demands a comprehensive acknowledgment of the extensive legal and medical data interwoven with the fabric of cannabis use for medical purposes.&#x0D; Unique Contribution to Theory, Practice and Policy: The study advocates for a holistic approach, urging policymakers to draw upon a repository of knowledge that transcends mere legal frameworks. In doing so, it seeks to bridge the gap between state and federal perspectives, fostering a more informed and cooperative discourse. Navigating the path toward resolution involves a delicate balance. The sculpting of legislative measures should be informed by a deep appreciation of the medical intricacies associated with cannabis use. The study underscores the significance of considering medical data as an essential compass in the journey toward harmonizing state and federal regulations. By acknowledging the complex interplay between legality and medical efficacy, this research contributes to the ongoing dialogue surrounding cannabis legislation. In conclusion, this study illuminates the intricate dance between state and federal jurisdictions in the realm of medical cannabis use. Through a meticulous examination of legislative intricacies and a robust consideration of legal and medical data, it endeavors to pave the way for a more harmonious coexistence between state and federal perspectives.

Performance Comparison of Robust PID and FOPID for an Inverse Response Process Model

This paper provides a quantitative comparison between the performance provided by the use of Proportional-Integral-Derivative (PID) and Fractional-Order Proportional-Integral-Derivative (FOPID) controllers for the control of second-order processes with inverse response based on the integrated absolute value of the error (IAE index). These controllers are designed in order to achieve an optimal performance in both, set-point tracking task and regulatory control operation, while robustness considerations are taken into account, and, therefore, also the trade-of between robustness and performance of the control system is considered. The performance comparison and example, show that FOPID controllers achieve higher performance that PID controllers mainly for processes with larger normalized dead times as well when more robustness should be considered in the closed-loop system. These advantages are due to the use of the fractional parameter µ associated to the derivative part of the FOPID controller.

PID/FOPID Tuning Rule Based on a Second Order Inverse Response Process Model with Robustness Considerations

This paper deals with the design of a proportional-integral-derivative (PID) and fractional-order proportional-integral-derivative (FOPID) controller tuning rule for second-order controlled processes with inverse response. The design of the rules takes into account specific levels of robustness and performance for both load disturbance rejection and set-point tracking tasks. In the first step, the optimal parameters for both controllers are obtained from optimizations that seek to minimize the error in both control modes while maintaining the robustness constraint. Subsequently, fitting functions to the optimal parameters are sought to capture their behavior with the possibility of finding optimal parameters with the tuning rule designed between certain ranges of the model parameters. For FOPID and PID controllers, the rule IRM-RoT (Inverse Response Model Robust Tuning) have been developed. The performance of the controllers was compared using examples, and the results showed that fractional order controllers provide a novel solution for inverse response dynamics in process control.

Relocated internal model control based cascade control strategy for stable and unstable systems with delay

To control unstable and stable plants a new series cascade control structure (SCCS) is proposed. The proposed SCCS incorporates two or three controllers (setpoint tracking controller, secondary disturbance rejection controller and stabilising controller for only unstable plants). Internal model control scheme (IMCS) augmented with integration of square error (ISE) minimisation along with robustness considerations are applied to design the secondary loop disturbance rejection controller. In contrast, relocated IMCS augmented with ISE minimisation is applied for the outer loop or setpoint tracking controller. Routh Hurwitz stability criteria and maximum sensitivity considerations are used to design the proportional-derivative (PD) controller, which stabilises the unstable plant. It is important to note that the suggested SCCS only needs four controller parameters to be tuned, which is less than the recently reported techniques. Robust stability analysis of the proposed SCCS is performed in the presence of uncertainty. The simulation results depict that the suggested approach imparts pre-eminent improvement in closed-loop performance such as faster setpoint tracking and better disturbance rejection for nominal and perturbed plants.

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Landing of supersonic transport (SST) suffers from a large uncertainty due to its highly sensitive aerodynamic properties in the subsonic domain, as well as the wind gusts around runways. At the vehicle design stage, a landing trajectory optimization under wind uncertainty in a multi-objective solution space is desired to explore the possible trade-off in its key flight performance metrics. The proposed algorithm solves this robust constrained multi-objective optimal control problem by integrating non-intrusive polynomial chaos expansion into a constrained evolutionary algorithm. The computationally tractable optimization is made possible through the conversion of a probabilistic problem into an equivalent deterministic representation while maintaining a form of the multi-objective problem. The generated guidance trajectories achieve a significant reduction of the uncertainty in their terminal states with a marginal modification in the control history of the deterministic solutions, validating the importance of the consideration of robustness in trajectory optimization.

IPCADP-Equalizer: An Improved Multibalance Privacy Preservation Scheme against Backdoor Attacks in Federated Learning

Although there are some protection mechanisms in federated learning, its training process is still vulnerable to some powerful attacks, such as invisible backdoor attacks. Existing research work focuses more on how to prevent attacks in distributed training scenarios and improve the security of the FL training process, but it lacks consideration of utility and robustness, especially when the learning model of FL suffers from stealth backdoor attacks. This paper proposes an improved FL defense scheme IPCADP based on user-level differential privacy and variational autoencoders technology. The scheme can control and protect the privacy attribute of the image and can also eliminate the triggers that exist in the poisoned image. The experimental results show that compared with some existing defense schemes, IPCADP can defend against invisible backdoor attacks and improve the classification accuracy of the main task, while mitigating the impact of attacks on model robustness and stability. To a certain extent, the balance and unity of security, utility, and robustness are realized.

Generalized optimal 3-degree of freedom parallel cascade control strategy for stable, unstable and integrating chemical processes

To control stable, unstable and integrating chemical processes, a new three-degree of freedom (3DOF) decoupled parallel cascade control architecture (PCCA) is proposed. The proposed PCCA assimilates two/three controllers (secondary disturbance rejection controller, stabilizing controller for only unstable/integrating plants and set-point tracking controller). Internal model control approach (IMCA) augmented with integral of squared error (ISE) minimization and robustness considerations are adopted to design both the secondary loop disturbance rejection controller and the outer-loop set-point tracking controller with the satisfactory performance-robustness tradeoff. Maximum sensitivity constraints and Routh-Hurwitz stability criteria are applied to design the proportional-derivative (PD) controller which stabilizes the unstable and integrating primary plant. It is worth mentioning that the suggested decoupled 3DOF PCCA requires tuning of only two/three controller parameters which are less when compared to recently reported strategies. Robust stability of the suggested 3DOF PCCA is studied in the presence of uncertainty. The simulation results epitomize that the suggested methodology imparts pre-eminent enhancement in closed-loop performance (faster setpoint following and disturbance elimination) for both nominal as well as perturbed plants.

Sudden column removal in tall buildings with flat slabs supported on core and perimeter columns

This paper focuses on robustness considerations of tall buildings with reinforced concrete flat slabs supported by a central core and one row of perimeter columns. This layout is commonly used in office and residential buildings to reduce storey height and maximise natural daylighting. The core and lateral bracing of tall buildings is generally less sensitive to local failure than the secondary load transfer system provided by the floors and perimeter columns, considering the high vulnerability of perimeter columns to accidental actions and punching of the slab around the supports. This paper analyses sudden corner and edge column removal situations in slab-core-perimeter column systems using dimensions and loading representative of current tall building design and construction, looking at potential punching at the connections and flexural failure in the slab. The dynamic punching model previously developed by the authors, was validated in this work for further cases of punching around edge columns and slabs with low amount of flexural reinforcement. The dynamic punching model is based on the Critical Shear Crack Theory for quasi-static punching and the Ductility-Centred Robustness Assessment method. A simplified level of approximation is proposed for a preliminary dynamic punching check during the conceptual design stage to optimise the position columns and level of flexural and punching reinforcement needed. The approaches proposed are consistent with Model Code 2010 and can be used to arrest the horizontal propagation of failure in the slab. The analyses of the case studies in this work showed that removal of an edge column adjacent to a corner column can be critical and alternative robustness design considerations are needed in this case.

Contending With Censorship In Canadian-Accredited Schools Abroad

In this study, one current and three former British Columbia (BC) offshore school principals were interviewed to seek their insights on how they contended with being compelled to censor material and disallow topics of conversation while administering a Canadian curriculum in an international context. Using a research design consistent with an interpretative phenomenological analysis (IPA) methodological framework, the data were interpreted using three reduction cycles to generate five categories: disillusionment, anger, struggle, expedience, and subversion. The participants’ responses were synthesized through the five categories in light of the phenomenon of moral distress, which occurs when a person is hindered from following a course of action consonant with their own moral judgement. Participants’ reflections on leading Canadian high schools outside of Canada offered meaningful insights into their lived experiences abroad and provided a basis for a more robust consideration of how principals make sense of morally distressing situations in their schools.