Corroding consensus-building: how self-centered public diplomacy is damaging diplomacy and what can be done about it

Public diplomacy (PD) is an activity which has become central to the analysis of modern diplomacy. Yet while there are common definitions of PD widely used internationally, practice between states has come to diverge more and more. There is disagreement in the academic literature about what should be included in PD activities, the actors, and boundaries. But there is little analysis of the effects of PD on mainstream diplomacy. This paper, written by a diplomat and sometime practitioner of PD, argues that PD is losing its connection with wider diplomacy which is based on reciprocity and consensus-building. The digital revolution has enabled PD self-promotion which diminishes the necessity for diplomatic partnering. Global rivalries are played out daily for global publics with little room for quiet reflection and compromise. Such self-centered PD has immersed itself in the confusing and divisive nature of online engagement. While the Internet has brought massive benefits and opportunities to both diplomacy and PD, the consensus-building part of true diplomatic engagement is receding. The activities of ISIS and Russia were just the first major collective challenges to diplomacy through new PD techniques. In the past, diplomacy has responded to crises and conflicts and rebuilt its options. Now PD’s chaotic and troubling evolution needs a new response. This should include partners in the non-state sector and the owners of technology platforms. The article takes a practitioner’s perspective and proposes a forum where state and non-state experts could discuss appropriate collective responses by diplomacy so it can reassert options available for consensus-building.Accepted manuscrip

The Political Economy of Growth and Governance

There are diverse ideas about governance around the world, and this paper studies them through the following questions: (a) what does the available evidence tell us about the political and institutional requirements for sustained economic growth? (b) What do we need from the state to secure growth? (c) How do a country’s internal characteristics support or impede its growth? (d) How does the external environment of a country influence its economic growth prospects? These elements are then put together into a model of growth, from which we derive conclusions about governance arrangements. Thus the paper outlines a simple framework within which to think about the political economy of growth that can be summed up in five points: good government, with secure political conditions; credible macroeconomic stability; savings and investment high enough to sustain adequate growth; openness to the world economy; and the discipline of external engagement. It then argues that the growth model needs to be underpinned by suitable governance arrangements, and suggests that good governance has two main elements, each quite complex in practice, namely: protection of property rights, and accountability of government.political economy, global economy, economic growth, governance, macroeconomic stability, property rights

Automatically Annotating the MIR Flickr Dataset: Experimental Protocols, Openly Available Data and Semantic Spaces

The availability of a large, freely redistributable set of high-quality annotated images is critical to allowing researchers in the area of automatic annotation, generic object recognition and concept detection to compare results. The recent introduction of the MIR Flickr dataset allows researchers such access. A dataset by itself is not enough, and a set of repeatable guidelines for performing evaluations that are comparable is required. In many cases it also is useful to compare the machine-learning components of different automatic annotation techniques using a common set of image features. This paper seeks to provide a solid, repeatable methodology and protocol for performing evaluations of automatic annotation software using the MIR Flickr dataset together with freely available tools for measuring performance in a controlled manner. This protocol is demonstrated through a set of experiments using a “semantic space” auto-annotator previously developed by the authors, in combination with a set of visual term features for the images that has been made publicly available for download. The paper also discusses how much training data is required to train the semantic space annotator with the MIR Flickr dataset. It is the hope of the authors that researchers will adopt this methodology and produce results from their own annotators that can be directly compared to those presented in this work

Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces

This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library

Salient Regions for Query by Image Content

Much previous work on image retrieval has used global features such as colour and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics. This paper discusses how this problem can be circumvented by using salient interest points and compares and contrasts an extension to previous work in which the concept of scale is incorporated into the selection of salient regions to select the areas of the image that are most interesting and generate local descriptors to describe the image characteristics in that region. The paper describes and contrasts two such salient region descriptors and compares them through their repeatability rate under a range of common image transforms. Finally, the paper goes on to investigate the performance of one of the salient region detectors in an image retrieval situation

Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces

Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval