Suchergebnisse

Avec la numérisation croissante des mondes marchands, les activités de consommation les plus banales provoquent la production d'une grande quantité de données. Pour les acteurs socioéconomiques, ces mégadonnées sont considérées comme des outils de maîtrise de l'avenir, les amenant à s'équiper d'un arsenal de machines qui ont pour fonction de produire des futurs économiques. Dans cet article, nous proposons d'examiner la manière dont ces machines participent à instituer une nouvelle culture du futur, laquelle se trouve à la croisée des logiques de prédiction et de prévision.

The majority of experts agree to say that the big data is a rupture. Maybe are they right. But this rupture is not really material, nor even organizational. It has already been a long time that the big web actors daily exploring and exploiting the big data. If revolution there is, it is happening elsewhere, at the periphery of the great disruption that depict most of big data promoters. To being aware of, simply ask the following question: pointing the revolutionary nature of big data and devices are provided to treat, what these actors are they doing? They are preparing a massive integration of artificial intelligences within the various spheres of society. If there is a rupture, it is therefore rather here: in this movement that we know today and which consists, for a great diversity of socioeconomic actors, to appropriate of the calculation agents that are increasingly autonomous and powerful. So in order to better understand the issues of this democratization, we propose in this thesis to study the case of machines to produce of the economic futures: what is their role within the socio-technical collectives that compose the markets? To answer this question, we will draw on a multi-situated ethnography we conducted from 2012 to 2015 according to a posture situated at the intersection of market, science and technology sociologies. Specifically, we will be mobilizing a corpus of archives and an important investigative material collected from several professionals, companies and salons to discuss the design and operation of these machines to predict merchant futures. We will see at the level of design environment, these machines are interesting in so far as they generally have a local intelligence that has to happen, in the present, of futures allowing to optimize the economic interests of those that implement. Starting from a series of studies and experimentations dealing with the use of a recommendation agent, we will show that this intelligence is debatable because it may entail of considerable ambivalences ...

The majority of experts agree to say that the big data is a rupture. Maybe are they right. But this rupture is not really material, nor even organizational. It has already been a long time that the big web actors daily exploring and exploiting the big data. If revolution there is, it is happening elsewhere, at the periphery of the great disruption that depict most of big data promoters. To being aware of, simply ask the following question: pointing the revolutionary nature of big data and devices are provided to treat, what these actors are they doing? They are preparing a massive integration of artificial intelligences within the various spheres of society. If there is a rupture, it is therefore rather here: in this movement that we know today and which consists, for a great diversity of socioeconomic actors, to appropriate of the calculation agents that are increasingly autonomous and powerful. So in order to better understand the issues of this democratization, we propose in this thesis to study the case of machines to produce of the economic futures: what is their role within the socio-technical collectives that compose the markets? To answer this question, we will draw on a multi-situated ethnography we conducted from 2012 to 2015 according to a posture situated at the intersection of market, science and technology sociologies. Specifically, we will be mobilizing a corpus of archives and an important investigative material collected from several professionals, companies and salons to discuss the design and operation of these machines to predict merchant futures. We will see at the level of design environment, these machines are interesting in so far as they generally have a local intelligence that has to happen, in the present, of futures allowing to optimize the economic interests of those that implement. Starting from a series of studies and experimentations dealing with the use of a recommendation agent, we will show that this intelligence is debatable because it may entail of considerable ambivalences ...

International audience ; La publicité est souvent considérée comme un vecteur de représentations stéréotypées qui participent à naturaliser l'attention des consommateurs. Une des promesses des technologies big data est de permettre aux offreurs de se défaire de ces formes d'essentialisation naïve en les amenant à rationaliser la conception de leurs communications. À travers une enquête de 36 mois reposant sur un ensemble de méthodes croisées nous montrons que les usages que les marchands font des technologies big data recouvrent des formes d'essentialisation naïve mais aussi et surtout d'essentialisation positive qui sont lourdes d'enjeux cognitifs et politiques. Abstract : Advertising is often seen as a vector of stereotypical representations that contribute to naturalizing the attention of consumers. A promise of big data technologies is to enable suppliers to discard of these forms of naive essentialisation by leading them to rationalize the design of their communications. Through an investigation of 36 months based on a set of crossed methods we show that the uses that the merchants make of big data technologies cover forms of naive essentialisation but also and especially of positive essentialisation which are heavy cognitive and political issues.

International audience ; La publicité est souvent considérée comme un vecteur de représentations stéréotypées qui participent à naturaliser l'attention des consommateurs. Une des promesses des technologies big data est de permettre aux offreurs de se défaire de ces formes d'essentialisation naïve en les amenant à rationaliser la conception de leurs communications. À travers une enquête de 36 mois reposant sur un ensemble de méthodes croisées nous montrons que les usages que les marchands font des technologies big data recouvrent des formes d'essentialisation naïve mais aussi et surtout d'essentialisation positive qui sont lourdes d'enjeux cognitifs et politiques. Abstract : Advertising is often seen as a vector of stereotypical representations that contribute to naturalizing the attention of consumers. A promise of big data technologies is to enable suppliers to discard of these forms of naive essentialisation by leading them to rationalize the design of their communications. Through an investigation of 36 months based on a set of crossed methods we show that the uses that the merchants make of big data technologies cover forms of naive essentialisation but also and especially of positive essentialisation which are heavy cognitive and political issues.

Cet article aborde un type particulier d'outils d'automatisation des enquêtes sur les consommateurs : les systèmes ou agents de recommandation. L'étude de ce cas particulier permet de mieux comprendre comment ont pu évoluer, au cours du temps, d'une part les systèmes d'automatisation des enquêtes sur les consommateurs et, d'autre part, les enjeux contemporains qui sont liés au déploiement des innovations réalisées dans ce domaine. L'article montre d'abord que l'automatisation actuelle des enquêtes sur les consommateurs se fonde sur l'avènement d'un nouveau régime cognitif qui repose sur la captation et l'exploitation de l'attention des consommateurs. Il présente ensuite les techniques d'intelligence artificielle mises en œuvre pour mobiliser les consommateurs. Il examine enfin les problèmes que soulèvent ces technologies, mais aussi quelques pistes susceptibles d'aider à les surmonter.

International audience ; The MOOC constitute pedagogical innovations that are often presented as radical. For their promoters, these innovations have the advantage of promoting a real democratization of knowledge. Considering the scientific literature, this promise could be one of the myths that underlies the development of digital technologies in the educational sphere. Thus, in order to better identify and understand how the uses of MOOC may or may not participate in producing inequality, we propose to explore the influence of socio-demographic factors on on-line training courses. To do this, we will rely on the analysis of traces of activity generated by users of the course "Manage your prices" which is hosted by digital education platform FutureLearn. Our goal will be to show how the uses that e-learners make of this course are shaped by the weight of social structures. ; Les MOOC sont des innovations pédagogiques qui sont souvent présentées comme radicales. Pour leurs promoteurs, ces innovations ont l'avantage de favoriser une véritable démocratisation des savoirs. Au regard de la littérature scientifique, cette promesse pourrait constituer un des mythes qui sous-tendent le développement des technologies numériques au sein de la sphère éducative. Afin de mieux identifier et comprendre comment les usages des MOOC peuvent ou non participer à la production d'inégalités, nous proposons dans cet article d'explorer l'influence des facteurs sociodémographiques sur les parcours de formation en ligne. Pour ce faire, nous nous appuierons sur l'analyse des traces d'activité produites par les utilisateurs du cours « Manage your prices » qui est hébergé par la plateforme d'éducation numérique FutureLearn. Notre objectif sera de montrer comment les usages que les e-apprenants font de ce cours sont façonnés par le poids des structures sociales.