Rivers all over the world are deteriorating in a fast rate. As a response, movements in the defence of rivers emerge and aim to restore and protect rivers. One of these defence strategies is to politicise fish to generate arguments for the protection of rivers, drawing from a fish-friendly river imaginary. The concept of river imaginaries describes that power is exercised through and by knowledge generated in truth regimes. In this poster presentation, we elaborate on two cases in which fishing people and their allies use a variety of truth strategies, resonating with specific fish-friendly river imaginaries. Both case studies are influenced by harmful mining and industry practices that pollute the river and wetland.The Dutch case study of the Border Meuse river reveals that the main argument to politicise fish is that infrastructural interventions and hydropower is killing and damaging fish. Through knowledge generating on the amount of fish-death and the aquatic state, a knowledge agenda is set and power is exercised to stop harmful river activities. The Colombian case of the Zapatosa wetlands reveals that the main argument to politicise fish is that fish is the main source of food. Through knowledge generating that focusses on re-learning from past artisanal fishing strategies and biocultural adaptation, a knowledge agenda is set and power is exercised to stop harmful mining practices. Although these river movements are using truth regimes to defend rivers, counter facts, counter norms, and counter agendas in the defence of harmful practices remain to exist.
We investigated to what extent correctional officers were able to apply skills from their self-defence training in reality-based scenarios. Performance of nine self-defence skills were tested in different scenarios at three moments: before starting the self-defence training programme (Pre-test), halfway through (Post-test 1), and after (Post-test 2). Repeated measures analyses showed that performance on skills improved after the self-defence training. For each skill, however, there was a considerable number of correctional officers (range 4–73%) that showed insufficient performance on Post-test 2, indicating that after training they were not able to properly apply their skills in reality-based scenarios. Reality-based scenarios may be used to achieve fidelity in assessment of self-defence skills of correctional officers.Practitioner summary: Self-defence training for correctional officers must be representative for the work field. By including reality-based scenarios in assessment, this study determined that correctional officers were not able to properly apply their learned skills in realistic contexts. Reality-based scenarios seem fit to detect discrepancies between training and the work field. Abbreviations: DJI: Dutch National Agency for Correctional Insitutes; ICC: Intraclass Correlation Coefficient.
We investigated the effects of reflex-based self-defence training on police performance in simulated high-pressure arrest situations. Police officers received this training as well as a regular police arrest and self-defence skills training (control training) in a crossover design. Officers' performance was tested on several variables in six reality-based scenarios before and after each training intervention. Results showed improved performance after the reflex-based training, while there was no such effect of the regular police training. Improved performance could be attributed to better communication, situational awareness (scanning area, alertness), assertiveness, resolution, proportionality, control and converting primary responses into tactical movements. As officers trained complete violent situations (and not just physical skills), they learned to use their actions before physical contact for de-escalation but also for anticipation on possible attacks. Furthermore, they learned to respond against attacks with skills based on their primary reflexes. The results of this study seem to suggest that reflex-based self-defence training better prepares officers for performing in high-pressure arrest situations than the current form of police arrest and self-defence skills training. Practitioner Summary: Police officers' performance in high-pressure arrest situations improved after a reflex-based self-defence training, while there was no such effect of a regular police training. As officers learned to anticipate on possible attacks and to respond with skills based on their primary reflexes, they were better able to perform effectively.
Prompt and timely response to incoming cyber-attacks and incidents is a core requirement for business continuity and safe operations for organizations operating at all levels (commercial, governmental, military). The effectiveness of these measures is significantly limited (and oftentimes defeated altogether) by the inefficiency of the attack identification and response process which is, effectively, a show-stopper for all attack prevention and reaction activities. The cognitive-intensive, human-driven alarm analysis procedures currently employed by Security Operation Centres are made ineffective (as opposed to only inefficient) by the sheer amount of alarm data produced, and the lack of mechanisms to automatically and soundly evaluate the arriving evidence to build operable risk-based metrics for incident response. This project will build foundational technologies to achieve Security Response Centres (SRC) based on three key components: (1) risk-based systems for alarm prioritization, (2) real-time, human-centric procedures for alarm operationalization, and (3) technology integration in response operations. In doing so, SeReNity will develop new techniques, methods, and systems at the intersection of the Design and Defence domains to deliver operable and accurate procedures for efficient incident response. To achieve this, this project will develop semantically and contextually rich alarm data to inform risk-based metrics on the mounting evidence of incoming cyber-attacks (as opposed to firing an alarm for each match of an IDS signature). SeReNity will achieve this by means of advanced techniques from machine learning and information mining and extraction, to identify attack patterns in the network traffic, and automatically identify threat types. Importantly, SeReNity will develop new mechanisms and interfaces to present the gathered evidence to SRC operators dynamically, and based on the specific threat (type) identified by the underlying technology. To achieve this, this project unifies Dutch excellence in intrusion detection, threat intelligence, and human-computer interaction with an industry-leading partner operating in the market of tailored solutions for Security Monitoring.
