Cocoa production, farmlands, and the galamsey: Examining current and emerging trends in the ASM-agriculture nexus

Data availability: The data that has been used is confidential.Copyright © 2023 The Authors. In this paper, we build on the diverse discussions on the nexus between artisanal and small-scale mining and agriculture to examine emerging relationships between mining operators, smallholder cocoa farmers, and landowners in rural cocoa-growing communities. Empirically, we draw on fresh insights from in-depth interviews with loosely coupled chain actors in Ghana's cocoa and mining sectors, we found what we call ‘coerced to sell’ strategies deployed by miners in the acquisition of farmlands for their operations. We go further to shed light on the employment trajectories of the new breed of landless farmers, and the emerging diversification strategies of landowners. Implications of our findings for the policy and practice of ASM and farmlands are outlined

Advising the whole student: eAdvising analytics and the contextual suppression of advisor values

Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies

Using text-mining-assisted analysis to examine the applicability of unstructured data in the context of customer complaint management

Double DegreeIn quest of gaining a more holistic picture of customer experiences, many companies are starting to consider textual data due to the richer insights on customer experience touch points it can provide. Meanwhile, recent trends point towards an emerging integration of customer relationship management and customer experience management and thereby availability of additional sources of textual data. Using text-mining-assisted analysis, this study demonstrates the practicality of the arising opportunity with means of perceived justice theory in the context of customer complaint management. The study shows that customers value interpersonal aspects most as part of the overall complaint handling process. The results link the individual factors in a sequence of ‘courtesy → interactional justice → satisfaction with complaint handling’, followed by behavioural outcomes. Academic and managerial implications are discussed

Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain.

Based on its advanced computing capabilities and ubiquity, the smartphone has rapidly been adopted as a tourism travel tool.With a growing number of users and a wide varietyof applications emerging, the smartphone is fundamentally altering our current use and understanding of the transport network and tourism travel. Based on a review of smartphone apps, this article evaluates the current functionalities used in the domestic tourism travel domain and highlights where the next major developments lie. Then, at a more conceptual level, the article analyses how the smartphone mediates tourism travel and the role it might play in more collaborative and dynamic travel decisions to facilitate sustainable travel. Some emerging research challenges are discussed

A New Consumerism: The influence of social technologies on product design

Social media has enabled a new style of consumerism. Consumers are no longer passive recipients; instead they are assuming active and participatory roles in product design and production, facilitated by interaction and collaboration in virtual communities. This new participatory culture is blurring the boundaries between the specific roles of designer, consumer and producer, creating entrepreneurial opportunities for designers, and empowering consumers to influence product strategies. Evolving designer-consumer interactions are enabling an enhanced model of co-production, through a value-adding social exchange that is driving changes in consumer behaviour and influencing both product strategies and design practice. The consumer is now a knowledgeable participant, or prosumer, who can contribute to user–centered research through crowd sourcing, collaborate and co-create through open-source or open-innovation platforms, assist creative endeavors by pledging venture capital through crowd funding and advocate the product in blogs and forums. Social media- enabled product implementation strategies working in conjunction with digital production technologies (e.g. additive manufacture), enable consumer-directed adaptive customisation, product personalisation, and self-production, with once passive consumers becoming product produsers. Not only is social media driving unprecedented consumer engagement and significant behavioural change, it is emerging as a major enabler of design entrepreneurship, creating new collaborative opportunities. Innovative processes in design practice are emerging, such as the provision of digital artifacts and customisable product frameworks, rather than standardised manufactured solutions. This paper examines the influence of social media-enabled product strategies on the methodology of the next generation of product designers, and discusses the need for an educational response

Rethinking Privacy and Freedom of Expression in the Digital Era: An Interview with Mark Andrejevic

Mark Andrejevic, Professor of Media Studies at the Pomona College in Claremont, California, is a distinguished critical theorist exploring issues around surveillance from pop culture to the logic of automated, predictive surveillance practices. In an interview with WPCC issue co-editor Pinelopi Troullinou, Andrejevic responds to pressing questions emanating from the surveillant society looking to shift the conversation to concepts of data holders’ accountability. He insists on the need to retain awareness of power relations in a data driven society highlighting the emerging challenge, ‘to provide ways of understanding the long and short term consequences of data driven social sorting’. Within the context of Snowden’s revelations and policy responses worldwide he recommends a shift of focus from discourses surrounding ‘pre-emption’ to those of ‘prevention’ also questioning the notion that citizens might only need to be concerned, ‘if we are doing something “wrong”’ as this is dependent on a utopian notion of the state and commercial processes, ‘that have been purged of any forms of discrimination’. He warns of multiple concerns of misuse of data in a context where ‘a total surveillance society looks all but inevitable’. However, the academy may be in a unique position to provide ways of reframing the terms of discussions over privacy and surveillance via the analysis of ‘the long and short term consequences of data driven social sorting (and its automation)’ and in particular of algorithmic accountability

Abduction and Anonymity in Data Mining

This thesis investigates two new research problems that arise in modern data mining: reasoning on data mining results, and privacy implication of data mining results. Most of the data mining algorithms rely on inductive techniques, trying to infer information that is generalized from the input data. But very often this inductive step on raw data is not enough to answer the user questions, and there is the need to process data again using other inference methods. In order to answer high level user needs such as explanation of results, we describe an environment able to perform abductive (hypothetical) reasoning, since often the solutions of such queries can be seen as the set of hypothesis that satisfy some requirements. By using cost-based abduction, we show how classification algorithms can be boosted by performing abductive reasoning over the data mining results, improving the quality of the output. Another growing research area in data mining is the one of privacy-preserving data mining. Due to the availability of large amounts of data, easily collected and stored via computer systems, new applications are emerging, but unfortunately privacy concerns make data mining unsuitable. We study the privacy implications of data mining in a mathematical and logical context, focusing on the anonymity of people whose data are analyzed. A formal theory on anonymity preserving data mining is given, together with a number of anonymity-preserving algorithms for pattern mining. The post-processing improvement on data mining results (w.r.t. utility and privacy) is the central focus of the problems we investigated in this thesis