MétaCan
Menu
Back to cohort
Record W4240785654 · doi:10.5204/mcj.656

Mining

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

Bibliographic record

VenueM/C Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.180
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueM/C JournalSame topicMining Techniques and EconomicsFrench-language works237,207