‘Ten Years Ahead of His Time’: The East End Elegance of Martin Peters

Martin Peters was a successful footballer whose public persona matched the way he played the game, without fuss or fanfare. A key English player of the 1960s, he does not usually feature in discussions about the connection between fashion and football in that decade. The focus is usually placed on players with celebrity status, especially George Best. This paper, working at the intersection of sport and fashion history and cultural studies, broadens the discussion by giving consideration to the non-celebrity-type player. This is done via an examination of the off-field dress and style of Martin Peters. The case is made, from studying the sartorial presentation of Peters, that we can recognize a connection between the player and other young men who favoured a low-key identification with the Mod culture of the time. This position supports a shift within the cultural historical study of British youth and masculine identity from the spectacular to the unspectacular

Designing Women: Essentializing Femininity in AI Linguistics

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female stereotypes as they fulfill emotional labor and perform pink-collar tasks, whether through roles as therapists, query-fillers, or companions. This paper examines four specific chat-based AI --ELIZA, XiaoIce, Sophia, and Erica-- and examines how their feminine linguistic patterns are used to maintain the illusion of emotional understanding in regards to the tasks that they perform. Overall, chat-based AI fails to subvert gender roles, as feminine AI are relegated to the realm of emotional intelligence and labor

Food Pedagogies: Histories, Definitions and Moralities

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How to choose the endorser: An experimental analysis on the effects of fit and notoriety

The present study is focused on the endorser topic following two different paths: firstly, proposing an extension of the theoretical match-up model, enlarge it through two other potential types of consistency: the typicality fit and the imagery fit. Secondly, the present study aims verifies the applicability of the same framework to the emerging situation with a brand linked to a not well-known endorser (internal as the founder or external as a web influencer). An experimental 3*2 (fit typology*high/low notoriety) between subject analysis was conducted in the food service domain. It showed some interesting considerations.From a theoretical point of view, the first relevant finding is that endorsement might be assimilated to a co-branding strategy, confirming the match-up model as an effective theoretical framework in this domain as well, with significant differences among the three fit typologies investigated. The typicality fit reveals to be the less effective in increasing attitude and other behavioural effects on consumers in spite of the large adoption of this kind of fit by companies. Instead, the imagery fit, seems to be the most impactful in terms of positive word of mouth activation and viral communication activities, at the same level at the categorical one. Moreover, the categorical fit induces the wider range of positive effect on the dependent variables (attitudes, willingness to pay and willingness to buy). Another interesting contribution is that the presence of an appropriate fit (in particular the categorical one) is able to compensate the absence of endorser notoriety and, on the average, the usage of a very popular endorser from the same domain of the brand is not necessary more effective in comparison with a not well-known endorser form the same domain. This result is the peak of the present research from a managerial point of view, as it leads to consider the opportunity to support the emerging practices by which companies turn to not well-known people (disclosing the founder, or presenting some workers, or adopting a common consumer as an influencer). The endorser not well-known, but presented with an adequate story-telling might be the best choice: less onerous and more effective than a big unrelated celebrity

The Purloined Personality: Consumer Profiling in Financial Services

Although almost every sector of the U.S. economy practices consumer profiling,perhaps the most substantive challenge to consumer privacy is found in the activities surrounding the use and disclosure of consumer transaction data by the financial services industry. For that reason, this Comment focuses exclusively on consumer profiling in the context of the financial services sector, defined as banks, credit card issuers, brokerages, and insurance companies

Identifying Multiple Personalities in Large Language Models with External Evaluation

As Large Language Models (LLMs) are integrated with human daily applications rapidly, many societal and ethical concerns are raised regarding the behavior of LLMs. One of the ways to comprehend LLMs' behavior is to analyze their personalities. Many recent studies quantify LLMs' personalities using self-assessment tests that are created for humans. Yet many critiques question the applicability and reliability of these self-assessment tests when applied to LLMs. In this paper, we investigate LLM personalities using an alternate personality measurement method, which we refer to as the external evaluation method, where instead of prompting LLMs with multiple-choice questions in the Likert scale, we evaluate LLMs' personalities by analyzing their responses toward open-ended situational questions using an external machine learning model. We first fine-tuned a Llama2-7B model as the MBTI personality predictor that outperforms the state-of-the-art models as the tool to analyze LLMs' responses. Then, we prompt the LLMs with situational questions and ask them to generate Twitter posts and comments, respectively, in order to assess their personalities when playing two different roles. Using the external personality evaluation method, we identify that the obtained personality types for LLMs are significantly different when generating posts versus comments, whereas humans show a consistent personality profile in these two different situations. This shows that LLMs can exhibit different personalities based on different scenarios, thus highlighting a fundamental difference between personality in LLMs and humans. With our work, we call for a re-evaluation of personality definition and measurement in LLMs