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Record W2893353983 · doi:10.1089/omi.2018.0143

The Dark Side of the Moon: The Internet of Things, Industry 4.0, and The Quantified Planet

2018· review· en· W2893353983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOMICS A Journal of Integrative Biology · 2018
Typereview
Languageen
FieldMedicine
TopicLegal, Health, Environmental and COVID-19 Challenges
Canadian institutionsMultiple Sclerosis Society of Canada
Fundersnot available
KeywordsBig dataAutomationData scienceComputer scienceComputer securityEngineering

Abstract

fetched live from OpenAlex

Industry 4.0 is an innovation framework launched initially at the 2011 Hanover Fair in Germany. It is premised on extreme digital connectivity to build smart factories and deliver extreme automation in science and society. Industry 4.0 has recently scaled up worldwide beyond Germany and Europe. Industry 4.0 employs the Internet of Things (IoT) to connect, communicate, and collect big data from embedded sensors in living and inanimate objects. When we add artificial intelligence (AI) powered real-time data analyses to the IoT, a state of worldwide extreme connectivity, or "The Quantified Planet," is created. By its focus on connectivity at a systems scale, the Industry 4.0 is of interest to health products manufacturing and service automation in medicine, biology, ecology, and society. But there are also unchecked assumptions. This article poses a question that has so far been neglected in the Industry 4.0 innovation echo chamber. Is it always good to have pervasive connectivity and extreme integration to the point that "everything is connected to everything else"? For example, extreme connectivity creates an "All Your Eggs in One Basket" problem and thus potential for complete network collapse in a domino effect when a component in a highly integrated system fails. Digital connectivity cultivates a fertile ground for new social and political power structures for authoritarian governance (i.e., a worrisome state of "pansurveillance") by one person in total control of knowledge networks in science and society, directly or through connected proxies. Filter bubbles created by extreme digital connectivity and AI can result in compressed foresight, lackluster innovation, and monocultures in laboratory life. The way forward is to harness Industry 4.0 and connectivity without such digital network toxicity. Similar to fire exits in skyscrapers and other built environments, designing innovation systems with extreme connectivity demands that we also think of "exit plans" from the omnipresent digital networks, IoT, and The Quantified Planet-both for safety and sustained creativity. Finally, the relationship between new technologies and society is far from being a simple one-directional interaction; it is not the technologies that cause social disruption. On the contrary, it is the value-loaded decisions made by individuals, organizations, and other social actors that shape sociotechnical change. Recognizing the latter narrative is important because it informs how we do science and can best respond to uncertainties and opaque assumptions embedded in emerging technologies such as AI, IoT, and Industry 4.0.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.343
Teacher spread0.295 · 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