Selection Power and Selection Labor for Information Retrieval

This study examines the relation between selection power and selection labor for information retrieval (IR). It is the first part of the development of a labor theoretic approach to IR. Existing models for evaluation of IR systems are reviewed and the distinction of operational from experimental systems partly dissolved. The often covert, but powerful, influence from technology on practice and theory is rendered explicit. Selection power is understood as the human ability to make informed choices between objects or representations of objects and is adopted as the primary value for IR. Selection power is conceived as a property of human consciousness, which can be assisted or frustrated by system design. The concept of selection power is further elucidated, and its value supported, by an example of the discrimination enabled by index descriptions, the discovery of analogous concepts in partly independent scholarly and wider public discourses, and its embodiment in the design and use of systems. Selection power is regarded as produced by selection labor, with the nature of that labor changing with different historical conditions and concurrent information technologies. Selection labor can itself be decomposed into description and search labor. Selection labor and its decomposition into description and search labor will be treated in a subsequent article, in a further development of a labor theoretic approach to information retrieval.

A labor theoretic approach to information retrieval

This article synthesizes the labor theoretic approach to information retrieval. Selection power is taken as the fundamental value for information retrieval and is regarded as produced by selection labor. Selection power remains relatively constant while selection labor modulates across oral, written, and computational modes. A dynamic, stemming principally from the costs of direct human mental labor and effectively compelling the transfer of aspects of human labor to computational technology, is identified. The decision practices of major information system producers are shown to conform with the motivating forces identified in the dynamic. An enhancement of human capacities, from the increased scope of description processes, is revealed. Decision variation and decision considerations are identified. The value of the labor theoretic approach is considered in relation to pre-existing theories, real world practice, and future possibilities. Finally, the continuing intractability of information retrieval is suggested.

Description and search labor for information retrieval

Selection power is taken as the fundamental value for information retrieval systems. Selection power is regarded as produced by selection labor, which itself separates historically into description and search labor. As forms of mental labor, description and search labor participate in the conditions for labor and for mental labor. Concepts and distinctions applicable to physical and mental labor are indicated, introducing the necessity of labor for survival, the idea of technology as a human construction, and the possibility of the transfer of human labor to technology. Distinctions specific to mental labor, particular between semantic and syntactic labor, are introduced. Description labor is exemplified by cataloging, classification, and database description, can be more formally understood as the labor involved in the transformation of objects for description into searchable descriptions, and is also understood to include interpretation. The costs of description labor are discussed. Search labor is conceived as the labor expended in searching systems. For both description and search labor, there has been a progressive reduction in direct human labor, with its syntactic aspects transferred to technology, effectively compelled by the high relative costs of direct human labor compared to machine processes.

Human information retrieval

Information retrieval in the age of Internet search engines has become part of ordinary discourse and everyday practice: "Google" is a verb in common usage. Thus far, more attention has been given to practical understanding of information retrieval than to a full theoretical account. In Human Information Retrieval, Julian Warner offers a comprehensive overview of information retrieval, synthesizing theories from different disciplines (information and computer science, librarianship and indexing, and information society discourse) and incorporating such disparate systems as WorldCat and Google into a single, robust theoretical framework. There is a need for such a theoretical treatment, he argues, one that reveals the structure and underlying patterns of this complex field while remaining congruent with everyday practice. Warner presents a labor theoretic approach to information retrieval, building on his previously formulated distinction between semantic and syntactic mental labor, arguing that the description and search labor of information retrieval can be understood as both semantic and syntactic in character. Warner's information science approach is rooted in the humanities and the social sciences but informed by an understanding of information technology and information theory. The chapters offer a progressive exposition of the topic, with illustrative examples to explain the concepts presented. Neither narrowly practical nor largely speculative, Human Information Retrieval meets the contemporary need for a broader treatment of information and information systems.

Making the law accessible to non-lawyers: effects of different kinds of expertise on perceived usability of online legal information services

To help design an environment in which professionals without legal training can make effective use of public sector legal information on planning and the environment - for Add-Wijzer, a European e-government project - we evaluated their perceptions of usefulness and usability. In concurrent think-aloud usability tests, lawyers and non-lawyers carried out information retrieval tasks on a range of online legal databases. We found that non-lawyers reported twice as many difficulties as those with legal training (p = 0.001), that the number of difficulties and the choice of database affected successful completion, and that the non-lawyers had surprisingly few problems understanding legal terminology. Instead, they had more problems understanding the syntactical structure of legal documents and collections. The results support the constraint attunement hypothesis (CAH) of the effects of expertise on information retrieval, with implications for the design of systems to support the effective understanding and use of information.

TDM modelling and evaluation of different domain transforms for LSI

Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF 9/7) wavelet transform as a pre-processing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a pre-processing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

Thresholding of noisy shoeprint images based on pixel context

In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.