Search results

This study aims to explore the factors whereby some international organizations (IOs) are more effective than others in international mediation and proposes three types of hypotheses through combining quantitative and qualitative analysis. First, IOs with greater institutional capabilities for gathering, exchanging and disseminating conflict-related information are more likely to mediate effectively. IO bias is another factor of influence in this regard. Second, IOs with greater institutional capabilities for deploying field missions and guaranteeing agreement are more likely to mediate effectively and maintain durable peace. Third, IOs with higher amounts of leverage are more likely to mediate effectively.

The study establishes two data sets: one on interstate conflict; the other on intrastate conflict, thus to cover as many research samples as possible and avoid sampling bias.

Results of the statistical analysis indicate that no matter interstate or intrastate conflict, IOs with higher institutional capabilities for diplomatic interventions are more likely to bring conflict parties to an agreement and thereafter maintain short-term peace. IOs with higher institutional capabilities for economic sanctions are similarly effective. Furthermore, IOs with greater institutional capabilities for field mission deployment mediate more effectively, whether in facilitating peace agreements or maintaining short-term and long-term peace after the agreement. IO bias and preference, however, have no significant impact on mediation effectiveness.

This study has made no in-depth explorations of such existing and important research areas as different third-party comparisons of the mediation effect.

This paper attempts to make some contributions to the topic of mediation effectiveness through applying a bargaining model to the research and performing a statistical analysis based on both an interstate conflict data set and an intrastate conflict data set.

This paper provides an in-depth causal analysis and thoroughgoing comparison of the effectiveness of IOs in both interstate conflicts and intrastate conflicts.

The purpose of this paper is to examine how upstream social marketing can benefit from using social media commentary to identify cognitive biases. Using reactions to leading media/news publications/articles related to climate and energy policy in Australia, this paper aims to understand underlying community cognitive biases and their reasonings.

Social listening was used to gather community commentary about climate and energy policy in Australia. This allowed the coding of natural language data to determine underlying cognitive biases inherent in the community. In all, 2,700 Facebook comments were collected from 27 news articles dated between January 2018 and March 2020 using exportcomments.com. Team coding was used to ensure consistency in interpretation.

Nine key cognitive bias were noted, including, pessimism, just-world, confirmation, optimum, curse of knowledge, Dunning–Kruger, self-serving, concision and converge biases. Additionally, the authors report on the interactive nature of these biases. Right-leaning audiences are perceived to be willfully uninformed and motivated by self-interest; centric audiences want solutions based on common-sense for the common good; and left-leaning supporters of progressive climate change policy are typically pessimistic about the future of climate and energy policy in Australia. Impacts of powerful media organization shaping biases are also explored.

Through a greater understanding of the types of cognitive biases, policy-makers are able to better design and execute influential upstream social marketing campaigns.

The study demonstrates that observing cognitive biases through social listening can assist upstream social marketing understand community biases and underlying reasonings towards climate and energy policy.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.

Despite some recent progress, gender inequality remains a persistent problem in science, technology, engineering, mathematics and medicine (STEMM) organisations. This article seeks to better understand resistance to gender equality (GE) in this context with the aim of shedding light on the workplace-based impediments to equality and unlocking remediation opportunities.

The article draws on in-depth interviews with 20 STEMM leaders to examine how they talk about the problem of gender inequality in the organisations they lead. Because resistance is rarely expressed directly, we adopt an in-depth, granular approach to examining what we call STEMM leaders’ “resistance talk” by decoding expressions of GE resistance that may appear, ostensibly, as something else.

We found various ideas, arguments and other discursive practices which function to legitimate or justify the status quo. These are both described by leaders in relation to what they are dealing with in their own organisations and expressed themselves. While similar “legitimating discourses” operate in other gender-segregated workplaces, our findings show how they manifest specifically in STEMM contexts.

Our results provide much-needed granular level evidence of the discursive tactics deployed to legitimate the status quo and obstruct progress toward GE in STEMM. This extends understanding of barriers to GE in STEMM and, importantly, highlights where attention might be directed to both counter resistance and harness potentially changing attitudes to expedite the necessary change required for GE in STEMM.

The purpose of the study is focused on implementation of Global Navigation Satellite System (GLONASS) technique in civil aviation for recovery of aircraft position using Precise Point Positioning (PPP) method in kinematic mode.

The aircraft coordinates of Cessna 172 plane in XYZ geocentric frame were obtained based on GLONASS code and phase observations for PPP method. The numerical computations were executed in post-processing mode in the RTKPOST module in RTKLIB program. The mathematical scheme of equation observation of PPP method was solved using Kalman filter in stochastic processing.

In paper, the average accuracy of aircraft position is about 0.308 m for X coordinate, 0.274 m for Y coordinate, 0.379 m for Z coordinate. In case of the mean radial spherical error (MRSE) parameter, the average value equals to 0.562 m. In paper, the accuracy of aircraft position in BLh geodesic frame were also showed and described.

The PPP method can be applied for determination the coordinates of receiver, receiver clock bias, Zenith Wet Delay (ZWD) parameter and ambiguity term for each satellite.

The PPP method is a new technique for aircraft positioning in air navigation. The PPP method can be also used in receiver autonomous integrity monitoring (RAIM) module in aircraft-based augmentation system (ABAS) system in air transport. The typical accuracy for recovery the aircraft position is about cm ÷ dm level using the PPP method.

The paper is destined for people who work in area of geodesy, navigation, aviation and air transport.

The work presents the original research results of implementation the GLONASS satellite technique for recovery the aircraft position in civil aviation. Currently, the presented research PPP method is used in precise positioning of aircraft in air navigation based on global positioning system and GLONASS solutions.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

The authors evaluated the impact of an anti-bias training intervention for improving police behavior during interactions with community members and public perceptions of discrimination.

Fifty patrol officers from a diverse municipal agency were randomly selected to participate in an anti-bias intervention. Before and after the intervention, a random selection of Body Worn Camera (BWC) videos from the intervention group as well as from a control group of officers was coded using a validated tool for coding police “performance” during interactions with the public. Discrimination-based community member complaints were also collected before and after the intervention for treatment and control group officers.

The treatment group had a small but significant increase in performance scores compared to control group officers, F = 4.736, p = 0.009, R2ß < 0.01. They also had a small but significantly reduced number of discrimination-based complaints compared to control group officers, F = 3.042, p = 0.049, p2 = 0.015. These results suggest that anti-bias training could have an impact on officer behaviors during interactions with public and perceptions of discrimination.

Although these results are from a single municipal police department, this is the first study to suggest that anti-bias trainings may have a positive behavioral impact on police officers as well as the first to illustrate the potential for their impact on community members' perceptions of biased treatment by officers.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

Access to high-quality data is a challenge for humanitarian logistics researchers. However, humanitarian organizations publish large quantities of documents for various stakeholders. Researchers can use these as secondary data, but interpreting big volumes of text is time consuming. The purpose of this paper is to present an automated quantitative content analysis (AQCA) approach that allows researchers to analyze such documents quickly and reliably.

Content analysis is a method to facilitate a systematic description of documents. This paper builds on an existing content analysis method, to which it adds automated steps for processing large quantities of documents. It also presents different measures for quantifying the content of documents.

The AQCA approach has been applied successfully in four papers. For example, it can identify the main theme in a document, categorize documents along different dimensions, or compare the use of a theme in different documents. This paper also identifies several limitations of content analysis in the field of humanitarian logistics research and suggests ways to mitigate them.

The AQCA approach does not provide an exhaustive qualitative analysis of documents. Instead, it aims to analyze documents quickly and reliably to extract the contents’ quantifiable aspects.

Although content analysis has been used in humanitarian logistics research before, no paper has yet proposed an automated, step-by-step approach that researchers can use. It also is the first study to discuss specific limitations of content analysis in the context of humanitarian logistics.

Computing technology is becoming ubiquitous within modern society and youth use technology regularly for school, entertainment and socializing. Yet, despite societal belief that computing technology is neutral, the technologies of today’s society are rife with biases that harm and oppress populations that experience marginalization. While previous research has explored children’s values and perceptions of computing technology, few studies have focused on youth conceptualizations of this technological bias and their understandings of how computing technology discriminates against them and their communities. This paper aims to examine youth conceptualizations of inequities in computing technology.

This study analyzes a series of codesign sessions and artifacts partnering with eight black youth to learn about their conceptualizations of technology bias.

Without introduction, the youth demonstrated an awareness of visible negative impacts of technology and provided examples of this bias within their lives, but they did not have a formal vocabulary to discuss said bias or knowledge of biased technologies less visible to the naked eye. Once presented with common technological biases, the youth expanded their conceptualizations to include both visible and invisible biases.

This paper builds on the current body of literature around how youth view computing technology and provides a foundation to ground future pedagogical work around technological bias for youth.