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Enregistrement W4240785654 · doi:10.5204/mcj.656

Mining

2013· article· en· W4240785654 sur OpenAlexaboutno aff
Dean Laplonge, Axel Bruns

Notice bibliographique

RevueM/C Journal · 2013
Typearticle
Langueen
DomaineEngineering
ThématiqueMining Techniques and Economics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

Mining is an industry that likes to maintain a certain element of distance from the rest of the world. The practice of mining for minerals rarely takes place close to homes. If it weren’t for the sea of high-vis clothing that we now see pouring through many city airports in resource-rich countries such as Australia and Canada, and for the immense importance which the mining industry now plays for the economies of such countries, mining may well have remained in isolation for many decades to come. But the cocoon of safety which this industry has enjoyed from the wider world for so long is starting to crack. Discussions about mining—about the culture of mining—are starting to emerge. We are starting to hear about the environmental concerns, the gender issues, the difficulties of sustainability for companies and employees, the impacts of operations on indigenous cultures, the accumulation of wealth in the hands of a few, and so on. We are starting to hear stories of mining that do not suit the image of an industry that would no doubt wish to hold on to a (now dubious) reputation as the most successful outcome of man’s emergence into modern capitalist industrialisation; where man is the ultimate controller and everything is his (usually not hers) for the taking. And there appear to be no limits to what we are willing to mine. The literal interpretation of ‘mining’ is the extraction of something from the ground. But in addition to such mining of physical resources, the exploitation of information at large scale – now known as data mining – is also turning into a burgeoning industry, generating new intellectual and economic opportunities while raising complex ethical concerns. Here, too, many of the concerns now raised for resources mining apply, mirroring that debate surprisingly closely. Data mining has developed for some time well outside of public perception, in the labs of Yahoo!, Google, and other Internet giants of the second and third generation; but where it has risen to public attention it is increasingly seen as an uncontrolled and highly aggressive industry undergoing what may turn out to be unsustainable growth. Some of its leading exponents are criticised for dealing with their data sources with little concern for the privacy or ownership rights of those whom those data concern; and a picture of data miners as Mark Zuckerberg-style computer geeks with high skills and low morals persists and is part reality, part caricature. In all its forms, ‘mining’ therefore is easily associated with engineering, geology, mathematics, economics, sociology, and other scientific practices. But where are the cultural analyses of mining? The articles in this issue of M/C Journal are not based on the kind of ‘pure science’ that we might normally associate with mining. They are not written by those who work in the disciplines of engineering, geology, computer science or behavioural statistics. Instead, the articles show an application of feminist theory and queer theory to issues of gender and masculinity, of media, cultural, and communication studies perspectives to questions of identity and representation. They introduce an environmentalist narrative into the shale gas debate. And they talk about the relationship between the media and mining (in both senses of the term). Ultimately, the authors of these articles seek to introduce new ways of discussing ‘mining’. They attempt to launch new narratives to help challenge dominant understandings of what mining is and how it works. In doing so, they seek to take the debates about mining beyond those we might see appearing every day in our newspapers and on our televisions. To achieve this, the authors have been forced to create from scratch. While their work is grounded in particular disciplines and modes of thinking, there are times when it becomes obvious that they are tilling new ground. Their work is definitely exploratory, as all good mining projects should be at the start. But in this there is the hope that there will be enough in the findings to encourage others to dig deeper; and the hope that commercial mining ventures may take notice of what they have found. At times, we—as the editors—have struggled to reconcile the desire of the authors to create the new with the preference of the academic culture for research that can reference research. How do we always insist that there must be a reference to what has come before when we know—as the authors do—that there is nothing before and that we are breaking new ground. The interpretations of mining that emerge in the articles in this issue are new—sometimes only partially formed and undoubtedly open for wider debate. But that has always been the intent of this issue. Mining is everywhere today. In some ways, we have become obsessed with mining. Mining occupies discussions about the economy, the environment, the labour market, politics, land rights, the media, and personal identity. If we consider ‘mining’ through the lens of gender or ethnicity, through queer studies, poststructuralism or feminism, through the media or through critical accounts of academic intervention, what do we come up with? Are there ways of viewing mining that are not tied to the ‘hard’ sciences? What different views of mining emerge when we view it through a more comprehensive, multidisciplinary lens? This is what we asked of the authors and this is what they have produced. This issue of M/C Journal introduces some differing interpretations and application of ‘mining’. Specifically, the articles in this issue seek to re-envision mining as a cultural practice which can be read in multiple ways. It would have been easier to have asked for authors to talk about leadership styles, “best practice” processes or safety—all of these are familiar topics of debate in mining companies today. Or about surveillance, user-generated content, and precarious labour – all of them well-trodden paths in Internet research, paths which lead increasingly to the leaders of the emerging data mining industry. Less common—and often non-existent—are discussions that bring mining out of isolation and alongside issues of importance in and to the wider culture. This issue of M/C Journal, then, seeks to broaden the debate about mining in all its forms, physical or digital, and to open it up to new inputs, new discourses, new arguments. We are grateful to our authors and peer reviewers for their contributions to this issue, and invite you to dig in.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,507
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,180
Écart entre enseignants0,170 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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